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Traffic speed control using speed cameras

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Title:
Traffic speed control using speed cameras
Creator:
Galadari, Abdulla Ibrahim
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
Publication Date:
Language:
English
Physical Description:
95 leaves : illustrations ; 28 cm

Subjects

Subjects / Keywords:
Speed limits ( lcsh )
Radar in speed limit enforcement ( lcsh )
Radar in speed limit enforcement ( fast )
Speed limits ( fast )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Bibliography:
Includes bibliographical references (leaves 92-95).
Thesis:
Civil engineering
General Note:
Department of Civil Engineering
Statement of Responsibility:
by Abdulla Ibrahim Galadari.

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Source Institution:
|University of Colorado Denver
Holding Location:
|Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
55471700 ( OCLC )
ocm55471700
Classification:
LD1190.E53 2003m G34 ( lcc )

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Full Text
TRAFFIC SPEED CONTROL USING
SPEED CAMERAS
by
Abdulla Ibrahim Galadari
B.S., University of Colorado, 2002
A thesis submitted to the
University of Colorado at Denver
in partial fulfilment
of the requirements for the degree of
Master of Science
Civil Engineering
2003


This thesis for the Master of Science
degree by
Abdulla Ibrahim Galadari
has been approved
by


Galadari, Abdulla Ibrahim (M.S., Civil Engineering)
Traffic Speed Control Using Speed Cameras
Thesis directed by Professor Bruce Janson
ABSTRACT
This thesis compares the effects of the placement of three types of speed controllers,
actual photo radar cameras, dummy casings of photo radar cameras, and signs that falsely
claim the existence of photo radar cameras on drivers behaviour in speeding. The speed
data was obtained in three different timelines, before the placement of any of the
controllers, within two weeks immediately after placement, and at least one year from the
placement. A comparative study was made to see the effective change in the overall speeds
of traffic due to the controllers. The study has found that in the short-term, all types of
controllers have reduced speeds to some extent, though differendy from each other.
However, in the long-term, actual photo radar cameras have reduced speeds even more
effectively, while speeds enforced by other controllers become less effective.
This abstract accurately represents the content of the candidates thesis. I recommend its
publication.
BruceJanson


DEDICATION
This thesis is dedicated to everything and everyone, whether existing or not.


ACKNOWLEDGMENTS
I am sincerely grateful and thankful to the Almighty Creator of the Worlds. Through the
wisdom of un-wisdom will the Word of God be suppressed, for the Word of God is
Wisdom, and only the fools will detest.
Read! In the Name of your Lord, Who has created. Has created the human from a clot of
blood. Read! And your Lord is the Most Generous. Who has taught by the pen. Who has
taught the human that which he knew not. (Holy Quran, 96: 15)
For the Lord giveth wisdom, out of His mouth cometh knowledge and discernment; He
layeth up sound wisdom for the upright, He is a shield to them that walk in integrity; That
He may guard the paths of justice, and preserve the way of His godly ones. Then shalt
thou understand righteousness and justice, and equity, yea, every good path. For wisdom
shall enter into thy heart, and knowledge shall be pleasant unto thy soul. (Mishlei
[Proverbs], 2: 6 10, Tanakh [Old Testament])


TABLE OF CONTENTS
List of Charts...............................................................vii
List of Tables................................................................ix
Chapter
1. Introduction............................................................... 1
1.1 Topic and Overview.........................................................1
2. Literature Review..........................................................7
2.1 History of Automated Traffic Enforcement...................................7
2.2 Worldwide Reports on the Effectiveness of Automated Traffic Enforcement.8
3. Speed Enforcement..................................................... 12
3.1 Speed Enforcement Issues.................................................12
3.2 Necessary Factors for Alternative Speed Enforcement......................13
3.3 Factors that May Hinder the Use of Photo Radar Cameras...................15
4. Methodology...............................................................18
4.1 Collecting Data..........................................................18
4.2 Data Analyses............................................................19
5. Speed Data Analysis and Results...........................................20
5.1 Statistical Analysis......................................................20
5.2 Results for Fixed Photo Radar Cameras....................................20
5.3 Results for Dummy Photo Radar Cameras....................................24
5.4 Results for Signs Claiming the Existence of Fixed Photo Radar Cameras...27
5.5 Conclusion and Recommendations...........................................30
Appendix
A. Summary Charts and Tables of Traffic Speed Data of Monitored Roads........32
Bibliography..................................................................92
vi


LIST OF CHARTS
Chart A.l Road 1 Histogram Before Data.....................................32
Chart A.2 Road 1 Histogram Immediately After Data..........................33
Chart A.3 Road 1 Histogram After Data.................................... 34
Chart A.4 Road 2 Histogram Before Data.....................................36
Chart A.5 Road 2 Histogram Immediately After Data..........................37
Chart A.6 Road 2 Histogram After Data......................................38
Chart A.7 Road 3 Histogram Before Data.....................................40
Chart A.8 Road 3 Histogram Immediately After Data..........................41
Chart A.9 Road 3 Histogram After Data......................................42
Chart A. 10 Road 4 Histogram Before Data...................................44
Chart A.l 1 Road 4 Histogram Immediately After Data........................45
Chart A. 12 Road 4 Histogram After Data....................................46
Chart A.13 Road 5 Histogram Before Data....................................48
Chart A.14 Road 5 Histogram Immediately After Data....................... 49
Chart A.l5 Road 5 Histogram After Data.....................................50
Chart A. 16 Road 6 Histogram Before Data...................................52
Chart A.17 Road 6 Histogram Immediately After Data.........................53
Chart A.l8 Road 6 Histogram After Data.....................................54
Chart A.19 Road 7 Histogram Before Data....................................56
Chart A.20 Road 7 Histogram Immediately After Data.........................57
Chart A.21 Road 7 Histogram After Data.....................................58
Chart A.22 Road 8 Histogram Before Data.................................. 60
Chart A.23 Road 8 Histogram Immediately After Data.........................61
Chart A.24 Road 8 Histogram After Data.....................................62
Chart A.25 Road 9 Histogram Before Data....................................64
Chart A.26 Road 9 Histogram Immediately After Data.........................65
Chart A.27 Road 9 Histogram After Data.....................................66
Chart A.28 Road 10 Histogram Before Data...................................68
Chart A.29 Road 10 Histogram Immediately After Data........................69
Chart A.30 Road 10 Histogram After Data.................................. 70
Chart A.31 Road 11 Histogram Before Data...................................72
Chart A.32 Road 11 Histogram Immediately After Data........................73
Chart A.33 Road 11 Histogram After Data....................................74
Chart A.34 Road 12 Histogram Before Data................................. 76
Chart A.35 Road 12 Histogram Immediately After Data...................... 77
vii


Chart A.36 Road 12 Histogram After Data...................................78
Chart A.37 Road 13 Histogram Before Data..................................80
Chart A.38 Road 13 Histogram Immediately After Data.......................81
Chart A.39 Road 13 Histogram After Data...................................82
Chart A.40 Road 14 Histogram Before Data..................................84
Chart A.41 Road 14 Histogram Immediately After Data.......................85
Chart A.42 Road 14 Histogram After Data...................................86
Chart A.43 Road 15 Histogram Before Data..................................88
Chart A.44 Road 15 Histogram Immediately After Data.......................89
Chart A.45 Road 15 Histogram After Data...................................90
viii


LIST OF TABLES
Table 1.1 Major Causes of Death in Developed Regions...........................1
Table 1.2 Major Causes of Deaths in Developing Regions.........................2
Table 1.3 Major Causes of Death in the United States...........................3
Table 5.1 Effectiveness of Fixed Photo Radar Cameras in Controlling Speed of
Vehicles that Do Not Exceed the Speed Limit..........................21
Table 5.2 Effectiveness of Fixed Photo Radar Cameras in Controlling Speed of
Vehicles that Do Not Exceed 10 km/h over the Speed Limit.............22
Table 5.3 85th Percentile Speeds on Roads Monitored by Fixed Photo Radar
Cameras..............................................................23
Table 5.4 85th Percentile Speeds on Roads Monitored by Fixed Photo Radar
Cameras in Australia............................................... 24
Table 5.5 Effectiveness of Dummy Photo Radar Cameras in Controlling Speed
of Vehicles that Do Not Exceed the Speed Limit......................25
Table 5.6 Effectiveness of Dummy Photo Radar Cameras in Controlling Speed
of Vehicles that Do Not Exceed 10 km/h over the Speed Limit.........26
Table 5.7 85th Percentile Speeds on Roads with Dummy Photo Radar Cameras
Installed............................................................27
Table 5.8 Effectiveness of Signs Falsely Claiming the Existence of Fixed Photo
Radar Cameras in Controlling Speed of Vehicles that Do Not
Exceed the Speed Limit...............................................28
Table 5.9 Effectiveness of Signs Falsely Claiming the Existence of Fixed Photo
Radar Cameras in Controlling Speed of Vehicles that Do Not
Exceed 10 km/h over the Speed Limit..................................29
Table 5.10 85lh Percentile Speeds on Roads with Signs Falsely Claiming the
Existence of Fixed Photo Radar Cameras Installed..................30
Table A. 1 Road 1 Summary.....................................................35
Table A.2 Road 2 Summary......................................................39
Table A.3 Road 3 Summary.................................................... 43
Table A.4 Road 4 Summary......................................................47
Table A.5 Road 5 Summary......................................................51
Table A.6 Road 6 Summary......................................................55
Table A.7 Road 7 Summary.................................................... 59
Table A.8 Road 8 Summary......................................................63
Table A.9 Road 9 Summary......................................................67
Table A.10 Road 10 Summary....................................................71
IX


TableA.il Road 11 Summary........................................75
Table A.12 Road 12 Summary.......................................79
Table A.13 Road 13 Summary.......................................83
Table A.14 Road 14 Summary.......................................87
Table A.15 Road 15 Summary.......................................91
x


1. Introduction
1.1 Topic and Overview
As technology of mass production increases, more people have access to vehicles. With
the increase of the volume of vehicles in the streets of any metropolitan city or a town,
more traffic accidents are expected. In many countries, traffic accidents are considered
among the top ten main causes of death.
In some developing countries, the usage of motor vehicles per capita is very limited while
diseases, many of which are treatable, such as malaria, are the major causes of death.
Nevertheless, traffic accidents cause about 2% of all deaths for all ages in both developed
and developing countries. Tables 1.1 and 1.2, from the World Health Organisation, show
worldwide statistics comparing the ten major causes of death in developed and developing
countries, illustrating that traffic accidents cause about 2% of all deaths.
Table 1.1 Major Causes of Death in Developed Regions
Developed Regions %of Deaths

1 Ischaemic heart disease 24.7
2 Cerebrovascular disease 13.1
3 Trachea, bronchus, and lung cancer 4.8
4 Lower respiratory infections 3.5
5 Chronic obstructive pulmonary disease 3
6 Colon and rectum cancers 2.5
7 Stomach cancer 2.2
8 Traffic accidents 2
9 Suicide 1.8
10 Diabetes mellitus 1.6
1


Table 1.2 Major Causes of Deaths in Developing Regions
Developing Regions %of Deaths

1 Lower respiratory infections 9.9
2 Ischaemic heart disease 9
3 Cerebrovascular disease 7.5
4 Diarrhoeal diseases 7.4
5 Conditions during prenatal period 4.9
6 Tuberculosis 4.7
7 Chronic obstructive pulmonary disease 2.7
8 Measles 2.6
9 Malaria 2.2
10 Traffic accidents 1.9
In the United States, traffic accidents have been in the top major causes of death, though
deaths due to traffic accidents have decreased from 1960 to 2000 as seen in Table 1.3. In
many countries, including the United States and Western Europe, traffic accidents are the
leading cause of death of children and young adults.
2


Table 1.3 Major Causes of Death in the United States
Year Heart disease Cancer Cerebro- vascular diseases Chronic lower respiratory diseases Accidents
1970 492.7 198.6 147.7 21.3 62.2
1971 492.9 199.3 147.6 21.8 60.3
1972 490.2 200.3 147.3 22.8 60.2
1973 482 200 145.2 23.6 59.3
1974 458.8 201,5 136.8 23.2 52.7
1975 431.2 200.1 123.5 23.7 50.8
1976 426.9 202.5 117.4 24.9 48.7
1977 413.7 203.5 110.4 24.7 48.8
1978 409.9 204.9 103.7 26.3 48.9
1979 401.6 204 97.1 25.5 46.5
1980 412.1 207.9 96.4 28.3 46.4
1981 397 206.4 89.5 29 43.4
1982 389 208.3 84.2 2.1 40.1
1983 388.9 209.1 81.2 31.6 39.1
1984 378.8 210.8 78.7 32.4 39.8
1985 375 211.3 76.6 34.5 38.5
1986 365.1 211.5 73.1 34.8 38.6
1987 355.9 211.7 71.6 35 38.2
1988 352.5 212.5 70.6 36.5 38.9
1989 332 214.2 66.9 36.6 37.7
1990 321.8 216 65.5 36.3 37.2
1991 313.8 215.8 63.2 38 34.9
1992 306.1 214.3 62 37.9 33.4
1993 309.9 214.6 63.1 40.9 34.5
1994 299.7 213.1 63.1 40.6 34.6
1995 296.3 211.7 63.9 40.5 34.9
1996 288.3 208.7 63.2 41 34.9
1997 280.4 205.7 61.8 41.5 34.8
1998 272.4 202.4 59.6 42 35
1999 267.8 202.7 61.8 45.8 35.9
2000 257.5 200.5 60.2 44.9 33.9
Source:
J.S. National Center for Health Statistics, Vital Statistics of the United States, annual From
Statistical Abstract of the United States: 2002.
3


Traffic accidents are inevitable. They transpire for many different reasons. Some reasons
entail surface conditions, design, planning, construction, and operation. However, human
error is one of the major causes of most accidents and it is the deadliest of them all. This
entails poor judgement, lack of driving skills, failure to interact to existing roadway
conditions, aggressive driving, driving under the influence of narcotics or alcohol, and
unawareness of traffic rules.
Speeding is one of the main causes of traffic accidents resulted by human error by not
adhering to traffic rules of speed limits (Cowley, 1980, 1983, 1987; Dublin, 1982;
Sanderson and Cameron, 1982; Social Development Committee, 1991); the higher the
speed of a vehicle, the greater the risk of an accident (Insurance Institute of Highway
Safety, 1998). Speeding is the deadliest cause of accidents since the forces experienced by
the human body in a collision increase exponentially as the speed increases. According to
the laws of physics, speed and energy dissipation in a crash are directly related: for a given
vehicle mass, the kinetic energy to be dissipated in a crash increases by the square of the
impact speed: that is, if speed is doubled, four times the energy will be absorbed in the
crash. For this reason, it is said that increased speed leads to increased crash severity, and
probably increased injury severity to the vehicle occupants and a higher risk of fatalities.
Speeding is a deliberate and calculated behaviour where the driver knows the risk but
ignores the danger.
Many jurisdictions have tried to reduce the number of speeding cars by enforcing harsh
speeding laws. Different methods are used by different cities. This thesis focuses on three
different methods and identifies which of them would be the most effective to control
traffic speeds along streets and freeways. The three methods include (1) fixed photo
enforcement, (2) dummy photo cameras, and (3) signs falsely claiming the existence of
photo enforcement when they actually do not exist.
4


Fixed photo enforcement is an automated enforcement system defined as a technical
recording device triggered automatically by a speeding violation so that information about
the violating vehicle is recorded, making possible the subsequent identification of the
vehicle for purpose of sanctioning the owner or the driver. Speed cameras are developed
to detect and photograph speeding vehicles. Slant radar measures the speeds of passing
vehicles, while a camera control unit provides photographic evidence of the vehicle at the
scene of the offence and records the time, date, location, and travelling speed. Speed
cameras can be set so that both receding and/or approaching vehicles can be monitored.
The cameras are capable of taking two photographs per second and can be operated at any
time of the day (or night). Thus, it can be presumed that the cameras are effective to
identify most, if not all, vehicles that violate the speed laws on a specific portion of the
road.
Many countries have used photo radar speed enforcement for many years. The system has
been found to be very effective in reducing traffic, speeds. Several world reports on the
effectiveness of photo radar are discussed in this thesis to ensure the similarity between the
data used for this thesis and the international efficacy of the system.
The thesis tries to recognize a trend in drivers behaviour, if any, that would effectively
reduce the number of speeding vehicles to enforce the speed limits along a street or
freeway using the three mentioned methods. Cameron et al. (1992) made a study in
Australia identifying a covariance between speeding and alcohol consumption. The same
study also found a covariance between unemployment rates and alcohol consumption. The
study showed that motorists who drive under the influence of alcohol are less to be
affected by speed cameras. Knowing that alcohol levels in the blood can affect drivers
behaviour in regards to speed laws, the data used for this thesis is taken in parts of a city in
which alcohol consumption is extremely limited and a societal taboo. Hence, it is safe to
conclude that most drivers monitored are not under the influence of alcohol, and
5


therefore, mentally aware of the speed laws along the paths they are driving. However,
such an assumption would not be considered for the purposes of this thesis.
6


2. Literature Review
2.1 History of Automated Traffic Enforcement
Many studies worldwide have been made in regards to the effects of photo enforcement of
speed limits after their installation. However, not many researches has been made to
compare the other methods discussed in this thesis by having only signs that falsely claim
the existence of photo radars or having dummy photo radar cameras. Some research has
found that speeding is the main cause of all accidents, which is the source of the largest
costs in the loss of life, injuries, and property damage (Taylor, Lynam and Baruya, 2000;
Taylor, 2001; Taylor, Baruya and Kennedy, 2002).
The first example of automatic traffic control reported in the research literature (Lamm
and Kloeckner, 1984) was the photo-radar on Autobahn A3 between Cologne and
Frankfurt installed in May 1973. The system included three radar devices (one per lane)
mounted on a traffic sign bridge on a downgrade section with a very high accident rate.
The speed limits were 40 km/h for the right lane and 100 km/h for the other two. If the
speeds exceeded 45 km/h for the right lane and 110 km/h for the other two lanes, a
picture was automatically taken from the rear to fine the vehicles. In darkness, a flashlight
was used. The authors report that in 1982, after almost 10 years from operation, only 10%
object to the fine and that accident rates have been reported to have decreased effectively.
By 1997, according to a review by Glauz (1998) about 75 countries world-wide have used
photo radar for speed enforcement. Over the past couple of decades, automatic traffic
enforcement has been used extensively for speeding alone or for red light violations and
speeding at the same time. As of April 1996 there were 593 units in operation in Germany
(Glauz, 1998). The United Kingdom uses this technology to a great length. Speed control
by automated enforcement was tried out first at Twickenham bridge in 1990 and became
fully operational in the London area in 1992 (Toogood, 1993). According to Zaidel and
7


Makinen (1999) there were 520 automated enforcement cameras in London, divided about
equally between red light and speed enforcement. This alone makes up about one-fourth
of all automated enforcement cameras in the United Kingdom. The United Kingdom also
uses this technology to enforce bus-lane violations and for other different reasons.
Many countries including Switzerland, United States, Australia, Norway, Sweden, Canada,
Netherlands, Finland, Austria, Denmark, Ireland, Kuwait, United Arab Emirates, and New
Zealand have used automatic enforcement systems to detect traffic violators (Blackburn
and Gilbert, 1995; Coleman et al., 1996; Glauz, 1998; Fildes, 1995; Glad and 0stvik, 1991;
Nilsson, 1992).
2.2 Worldwide Reports on the Effectiveness of Automated
Traffic Enforcement
Different studies have been made in different parts of the world to identify the
effectiveness of photo radar enforcement for detecting speed violators. In 1997, about 25
million vehicle speed measurements were made in Australia. About 2.3% of all the vehicles
were speeding in areas where photo radar enforcements were installed. This was a
reduction from about 24% at the start of the installations in 1989.
Researchers at the Insurance Institute of Highway Safety (IIHS) measured travel speeds on
seven D.C. streets before photo radar cameras were installed and six months after their
deployment (Insurance Institute of Highway Safety, 2002). It has been shown that the
number of drivers travelling more than 10 mph over the speed limit dropped dramatically
at every site, with reductions ranging from 38 to 89 percent. Statistics from the D.C. police
department show that the percentage of vehicles aggressively speeding on D.C. streets and
highways has declined by more than 58% since the photo radar program started in July
2001 to about a year later. When the photo radar was first deployed, it had a warning
period for the first month. During this first month, 31% of the vehicles monitored by
photo radar exceeded the programs speeding threshold. After 9 months since the
8


deployment, only 13% of vehicles monitored by the radar have exceeded this threshold.
According to the Insurance Institute of Highway Safety, speeding is a major factor in
motor vehicle crashes, which are the leading cause of death for people under 34 years of
age in the United States.
Photo radar enforcement in the United States is only limited to about a dozen
communities due to more complex laws. Nevertheless, in 1995, a nationwide telephone
survey found that 57% of US residents favour using cameras to enforce speed limit laws
(Insurance Institute of Highway Safety, 1998).
Another study by the Insurance Institute of Highway Safety in British Columbia, Canada,
shows a 7% decline in crashes and up to 20% fewer deaths were recorded in the first year
of the deployment of the photo radar cameras. The proportion of speeding vehicles at
photo radar deployment stations in British Columbia declined from 66% in 1996 to less
than 40% in 1998. Researchers at the Insurance Institute of Highway Safety also attribute a
10% decline in daytime injuries to the photo radar enforcement. Extensive use of 54 photo
radar cameras is credited with reducing speeding from 23% of all vehicles to just 3.8% of
all vehicles in Victoria State, Australia. Researches in British Columbia, Australia, and
Texas established an association between the use of photo radar and speed reduction. A
1988 Victoria, B.C. study discovered that photo radar cameras reduced speeds at study
sites. Data from Victoria, Australia that was collected over two years showed that speed
reduction at camera sites was greater when media publicity and signs announced the
presence of photo radar. In Vancouver, B.C. research from a short-term 1994 study
indicated that fewer vehicles travelled over the speed limit when photo radar was in place
(Ministry of Transportation of Ontario, 1997).
A research in Australia shows that immediately after the introduction of photo radar
cameras, drivers on roads where the cameras were not installed dramatically reduced their
speeds. Researchers attribute this drop to drivers psychology for their fear that since other
9


roads had photo radar cameras installed that roads in the surrounding area has also been
introduced to the system, though that was not actually true. About six months since the
implementation, drivers in surrounding roads resumed their speeds. However, about a year
since the implementation, drivers reduced their speeds again in fear that the cameras are
being installed. Although the study shows that number of speeding vehicles on roads that
actually had cameras installed was less by about 5% from the overall volume than the
surrounding roads, surrounding roads still showed a significant decrease in the number of
drivers speeding over the posted speed limit. At camera sites, about 27% of vehicles were
over the speed limit compared to 40% before the camera installations. On surrounding
roads, about 32% of vehicles were reported to be travelling over the speed limit compared
to 40% before the cameras were installed in nearby roads (ARRB Transport Research,
2000, 2001).
A study of photo radar on a German autobahn to increase compliance with a 100 km/h
limit in the late 1970s reported increased compliance with the limit and resulted in a
reduction from 300 crashes, 80 injuries, and 7 fatalities to 9 crashes, 5 injuries, and no
deaths (Blackburn & Glanz, 1984; cited in Freedman, Williams and Lund, 1990).
Some researchers advocate the use of photo radar cameras extensively to be more
effective to reduce the overall drivers speed. Some studies in Arlington, Texas concluded
that the presence of photo radar cameras reduced speeding; the greater the concentration
of cameras, the greater the reduction in speeders (Ministry of Transportation of Ontario,
1997). Sweden, Germany, and Australia reported decreases in injury-producing collisions
with the introduction of photo radar. During a 1990-to-1992 Swedish research project,
data showed fewer injury-producing crashes both on test roadway sections monitored by
cameras and on control sections of roadways not monitored by cameras. The reductions
were greater, however, where there were cameras. German statistics compared collisions
on the Autobahn in 1977, without photo radar, and in 1978, after the installation of photo
radar. Researchers reported increased compliance with speed limits. Moreover, there were
10


only 9 crashes, 7 injuries, and no deaths in 1978 compared with 300 crashes, 80 injuries,
and 7 deaths the year before. Similarly, Australian statistics from 1992 and 1993 showed
photo radar reduced injury-producing collisions on some roadways by as much as 20
percent.
Several studies by the Ontario Ministry of Transportation have also concluded that photo
radar cameras are more effective with media publicity, including the use of signs. The next
chapter shows the reports from those studies as it may be useful to compare with studies
usage of signs falsely claiming the existence of photo radar cameras with the data being
reported by this thesis.
11


3. Speed Enforcement
3.1 Speed Enforcement Issues
Excessive speed has long been recognised as a major factor in road crashes, and has led to
great research into the factors which contribute to drivers speeding behaviour (Fildes and
Lee, 1993; French et al, 1993; Gregersen and Bjurulf, 1996; Harrison et al., 1998).
Enforcing speed limits requires sufficient funding and time. The issue of speed control is
to ensure the safety of the public. Hence, it is one of the top priorities of most
jurisdictions. However, the required funding for the process causes restrictions on the
methods used to control speed. Some jurisdictions dispatch traffic police that have the
authority to stop vehicles who make traffic violations, which includes speeding, to fine the
violators. Though this method is somewhat effective, it costs tremendous amounts of
money for every hour such law enforcement is dispatched. Also, the effectiveness of this
method is restricted to the time that the law enforcing officers were dispatched. They
would not be monitoring the same segment of a road all the time.
If most drivers drive at great speeds or disrespect law enforcement, then dispatching many
law enforcing vehicles on a daily basis in an attempt to reduce the speeding of many
vehicles would be deemed ineffective. In many cities, where speeding vehicles cause much
damage due to colossal accidents, different methods of speed control are being used. Some
cities use mobile radar cameras that are randomly placed clandestinely on the side of a road
that would take the photograph of the license plates of violating vehicles. This method
*
surprises many speeding drivers that may cause many drivers to take caution as they drive
on the roads.
Some jurisdictions place fixed photo radar cameras on the side of major roads, where
speeding vehicles are found in large amounts. This attempts to reduce the overall speed
12


violations that occur in such major roads during all times. Sometimes, signs are placed to
warn people in the existence of such cameras on the road, such that speeding vehicles start
reducing their speeds before reaching the photo radar cameras. This is done as not to
surprise speeding vehicles. Though speeding is dangerous, pushing the brakes at once
when a driver discovers a photo radar camera on the side of a freeway may even cause
further danger (Portans, 1988). Portans (1988) also concluded that media publicity is also
an important factor for reducing speeds if signs are chosen not to be used.
3.2 Necessary Factors for Alternative Speed Enforcement
Maintaining photo radar cameras can become very expensive, causing some jurisdictions to
use fake cameras or signs to fool drivers. In Boulder, Colorado for example, a programme
was initiated for cameras to be used to enforce red lights and speeding limits during green
time. In 2000, the programme cost $568,076 to operate, while only $463,803 was collected
in fine revenue (City of Boulder Agenda, 2000). Hence, it seems that deploying photo-
radar cameras are very expensive, and sometimes not as cost-effective as required. Though
this method may allow the reduction of accidents causing injuries, death, and property
damage, which will cost a lot more than the deployment of these cameras, jurisdictions are
liable to allocate public funding that needs to try to maintain the cost. Hence, if an
alternative to photo radar cameras can be found that will be as effective as the cameras but
not as costly, then jurisdictions would rather use those alternates. According to the City of
Boulders assessment of the effectiveness of the photo enforcement programs indicate that
they are highly effective in reducing speeding in direct proximity to the camera. The
limitation is that the speed reduction is only effective for a short distance directly in
advance of the device. One of the advantages reported is that it is less costly to operate
than mobile photo-radar cameras.
According to a British research, photo radar equipment is expensive to buy, operate and
maintain; the supporting prosecution procedures also incur substantial administrative
13


costs. However, the costs are small compared to the benefits to society and the economy
(Hooke, Knox and Portas, 1996). Local authorities and the police often have insufficient
funds to make fullest use of photo radar cameras to deal with speeding problems. Funds
for speed enforcement have to compete with other priorities from the limited budget
allocations, while some authorities simply cannot afford it. Some authorities consider using
alternative funding strategies to deal with the costs of installing, operating, and maintaining
photo radar cameras.
There are several types of costs and benefits that are regularly addressed by traffic agencies
to analyse the appropriateness of installing and maintaining speed cameras. Besides the
obvious costs of the equipment, installation, operation, and maintenance of the speed
cameras, there are costs associated to the courts or the agency prosecution service. There
are also more costs associated to publicity campaigns. In another note, the cost of planning
site locations for the cameras also burden agencies with more expenditure. Benefits are
more complex to estimate, since some benefits are intangible. There are savings in human
life and injury, as also savings from reduced property damage. There are savings
experienced by the police and emergency services as a result of attending to fewer road
accidents. There are savings experienced by the health services as a result of fewer
accidents. Income from fines generated is probably the simplest tangible benefits type that
can be estimated. With less accidents due to speeding, traffic flow will be improved,
reducing trip times and improved environment as a result of fewer emissions.
This thesis compares the effectiveness of the two other alternatives mentioned, dummy
cameras and false signs, with the actual photo radar cameras. For example, on roads that
are close to small airports, some jurisdictions may put signs that state the existence of
speed radar detection from airplanes. When drivers read the sign and see some small
airplanes flying or landing, they may be fooled to believe that some may actually be
detecting speeding vehicles, and consequendy reduce their speeds. In some cities that use
many photo radar cameras, sometimes also use fake cameras in some roads or segments of
14


a road that has actual cameras. Fake cameras are inserted within a casing to give the
appearance that an actual photo radar camera is in place. When drivers pass by those fake
'.cameras, some may be fooled and subsequently reduce their speeds. Such methods are
more economical than having actual photo radar cameras, since fake cameras or signs do
not require much maintenance. This thesis questions whether such methods are effective
to decrease the number of speeding violators.
3.3 Factors that May Hinder the Use of Photo Radar
Cameras
Some companies in countries that use photo radar extensively, such as the United
Kingdom, sell or provide information to the public about the locations of fixed photo
radar cameras. Some companies provide global coordinates of their locations that some
users record it in their global positioning tracking system to be able to circumvent a
speeding fine, by reducing speeds when nearing fixed photo radar locations. This
information allows people to speed in road segments that a fixed photo radar camera is
not installed. Traffic managers, in such cases, try to also use mobile photo radar cameras
that may surprise speeding drivers.
The information of the locations of mobile photo radar cameras can still be made public
when truckers may use the Citizen Band radio to warn other listeners on the whereabouts
of the cameras. There are some local radio stations that also accept calls from drivers who
give them information on the locations of the mobile cameras, as the locations are then
broadcasted over the airwaves to the public. Some traffic managers find that publicising
such information can render those cameras to be less effective. However, if publicising the
information will allow drivers to reduce their speeds as they near camera locations, would
that not mean that the cameras are doing their job? Though the public may know the
locations where those cameras may be found, they are not guaranteed that the cameras are
not found elsewhere.
15


According to the theory that suggests that publicising the existence of photo radar cameras
can allow the reduction of speed by drivers, some authorities place signs that warn drivers
in the existence of those cameras. The local governmental entity that manages traffic
deliberately publicises this information. Some jurisdictions in the United Kingdom and
Australia, for example, record the locations of fixed radar cameras in the public domain.
The main reason why photo radar cameras are installed is not to catch drivers unguarded,
but instead, to control their speeds. If publicity will cause that, then it may be effective. As
stated in the previous section, sometimes signs are placed to falsely claim the existence of
the photo radar cameras, only to try to manage drivers speed. Sometimes when new
cameras are installed, their locations are made to the public through the media (i.e.
newspapers, television, radio, internet).
The Ontario Ministry of Transportation made an eleven-month pilot study in the Province
of Ontario beginning on the summer of 1994 by using four portable photo radar units on
selected sections of roadways. The study used test sections that were equipped with photo
radar cameras and a control section not using the equipment. The roads used were diverse
in type. The sites included a 6 lane, 100 km/h divided freeway with urban commuter
traffic, a 4 lane, 100 km/h divided highway with recreational traffic, and a 2 lane, 80 km/h
undivided highway with urban commuter traffic. The loops embedded in the roadways
collected data 24 hours a day all days of the week on vehicle speeds and sizes. Researchers
concluded from the data that the proportion of speeding vehicles declined at all sites,
including controlled sites, but a greater speed decline was found in sites equipped with
photo radar cameras.
The reduction of speeds at control sites was attributed to the media coverage of the use of
photo radar at some sites, affecting drivers behaviour. Daily radio announcements
reminded drivers of the existence of photo radar cameras on the 6 lane road, which caused
it to have the greatest speed reductions. The other sites did not attract much media
attention. Hence, researchers have concluded that public media campaigns can lower
16


average speeds and the proportion of speeders. The study also found that for a short time,
the presence of signing that falsely claim the existence of photo radar cameras also reduced
drivers speeds. The study concluded that the existence of photo radar cameras and media
publicity would be most effective to reduce drivers speed.
17


4. Methodology
4.1 Collecting Data
The data collected for this research are the speeds of vehicles on roads that have in place
one of the three different methods to control speed, fixed radar detecting cameras, fake
cameras, and fake signs, that claim the existence of detecting cameras, when truly none are
in place.
Data for the speeds of vehicles were collected in three different time frames (1) before any
of the mentioned methods were installed, (2) within the first two weeks of their use, and
(3) after at least one year since they started to be used.
All the speed data collected were during levels of service B or better. This is to ensure that
vehicles had as close to free-flow conditions as possible to be certain that vehicles had the
ability to speed if they choose to, without having any hindrance or obstructions. The speed
data were collected in 15 minute time frames during randomly selected times of low levels
of service. The 15 minute random time frames were collected daily for about two weeks.
The data taken before any speed controllers were placed were collected at most 12 months
before any of the speed controllers were placed. Immediately within the first two weeks
since the speed controllers were placed, data was collected in a similar manner, during
times of low levels of service. More speed data were collected at least after 12 months
since the placement of the speed .controllers using the same process. All the data used for
this analysis was for the two leftmost lanes, since they are the lanes that are in the range of
the speed cameras. Hence, only the data from the two leftmost lanes of all the roads in this
study were analysed. All the roads in this with speed limits of less than 100 km/h had only
two lanes, while all other roads had 4 lanes, though only the data from the two leftmost
lanes were analysed.
18


Data was collected from different types of roads with different speed limits. Eight of the
roads used in the study have radar detector cameras, four of which are in an 80 kph speed
limit, one is in 110 kph speed limit, and three in a 120 kph speed limit zones. Four other
roads have dummy cameras, one of which is in a 60 kph speed limit, two in an 80 kph
speed limit, and another in a 120 kph speed limit zones. Three different roads have signs
placed claiming the existence of cameras when none were actually in place. In this
category, one road is in an 80 kph, 110 kph, and a 140 kph speed limit zones.
4.2 Data Analyses
Statistical analyses were done on each road to identify the significance of the change of
driver behaviour trend when fixed photo radar cameras, dummy cameras, and signs falsely
claiming the existence of cameras are installed. The speed data was analysed based mainly
on the following statistics (1) mean speed, (2) 85th percentile speed, (3) standard deviation,
(4) percentage of motorists driving under the speed limit, and (5) percentage of motorists
driving under 10 km/h over the speed limit.
19


5. Speed Data Analysis and Results
5.1 Statistical Analysis
As discussed in Section 4.2, different techniques of data analyses were made on the
collected data. This chapter compares the results of the effectiveness of the different
methods of speed control, photo radar cameras, dummy cameras, and false signs.
5.2 Results for Fixed Photo Radar Cameras
Results of the data analysed has shown that fixed photo radars are very effective in
controlling speed over a highway or street. Table 5.1 shows comparison of the vehicle
percentile that did not exceed the speed limit before, immediately, and after the installation
of fixed photo radar cameras. It can be observed from the data that this method is very
effective in reducing the numbers of speeding vehicles.
20


Table 5.1 Effectiveness of Fixed Photo Radar Cameras in Controlling Speed of
Vehicles that Do Not Exceed the Speed Limit
Speed Limit kph Percentile
Before Installation Immediately Installation After Installation
Road 2 80 30.22 53.29 68.81
Road 7 80 54.79 79.89 88.64
Road 9 120 55.20 80.84 90.21
Road 11 120 49.48 69.26 90.63
Road 12 120 47.66 50.48 86.40
Road 13 110 32.82 58.72 92.24
Road 14 80 47.52 72.18 77.23
Road 15 80 29.89 50.54 76.76
AVERAGE 43.45 64.40 83.87
In Table 5.2, the same data is compared for vehicles that did not exceed 10 km/h over the
speed limit. We can observe that on average about 98% of vehicles do not exceed 10 kph
after the installation of the fixed photo radar cameras. This is dramatically significant in
reducing the number of speeding vehicles. Before the installation of the photo radar
cameras, about only an average of 75% of vehicles travelled below 10 km/h over the speed
limit.
21


Table 5.2 Effectiveness of Fixed Photo Radar Cameras in Controlling Speed of
Vehicles that Do Not Exceed 10 km/h over the Speed Limit
10 kph Over Speed Limit Percentile
Before Installation Immediately Installation After Installation
Road 2 90 82.78 89.67 98.49
Road 7 90 99.82 99.54 99.73
Road 9 130 71.04 94.39 97.11
Road 11 130 68.78 85.07 99.03
Road 12 130 62.95 68.02 93.29
Road 13 120 44.82 84.83 98.10
Road 14 90 89.90 96.27 98.35
Road 15 90 76.79 91.24 99.62
AVERAGE 74.61 88.63 97.97
When comparing results from other studies in regards to the speed reduction attributed to
speed cameras, it has shown that on average, the 85th percentile speeds are dropped by
about 10%. Results from this study show a similar pattern. It is also apparent that the
higher the speed limit, the more percent change of speed is noticed. Seemingly, speed
limits of over 100 km/h show reduced speeds of more than 10%, while speed limits less
than 100 km/h show reduced speeds by less than 10%. This does not necessarily mean
that fixed radar cameras are more effective with higher speed limits. The speed limits that
are over 100 km/h in this study are freeways. Since the data taken for this study was made
during levels of service of B or better, many drivers on freeways speed relatively much
higher than drivers on other types of roads, since on other roads there are factors that may
help the reduction of speed, such as traffic lights or other physical restrictions.
Table 5.3 records the 85th percentile speeds of vehicles that were monitored by fixed photo
radar cameras that are used in this study. For comparison reasons, Table 5.4 shows 85th
percentile speeds of vehicles that were recorded in Australia. It is apparent that fixed photo
radar installations are more effective in roads with greater speed limits. Apparently, the
22


higher the speed limit, the more dramatic the change in the speeds of vehicles is observed,
when the road is controlled by the fixed radar cameras.
Table 5.3 85th Percentile Speeds on Roads Monitored by Fixed Photo Radar
Cameras
Speed Limit kph 85th Percentile
Before kph Immediately kph After kph
Road 2 80 91 87 84
Road 7 80 85 81 78
Road 9 120 139 121 118
Road 11 120 149 129 119
Road 12 120 148 138 119
Road 13 110 139 120 107
Road 14 80 88 83 82
Road 15 80 93 87 84
23


Table 5.4 85th Percentile Speeds on Roads Monitored by Fixed Photo Radar
Cameras in Australia
Speed Camera Site 85th Percentile- Speed Before Installation:-/ 85th Percentile Speed After Installation , _
Woy Woy Rd, Kariong (Speed limit 80 km/h) 88 km/h 78 km/h
Pacific Hwy, Herons Creek (Speed limit 100 km/h) 110 km/h 101 km/h -102 km/h
New England Hwy, Tilbuster (Speed limit 100 km/h) 110 km/h 99 km/h -102 km/h
Hume Hwy, Tarcutta (Speed limit 100 km/h) 111 km/h 106 km/h
Cowpasture Road, Green Valley (Speed limit 70 km/h) 80 km/h 67 km/h
Delhi Road, Macquarie Park (Speed limit 60 km/h) 71 km/h 66 km/h
New England Hwy, Lochinvar (Speed limit 60 km/h) 78 km/h 64 km/h
Bells Line of Road, Kurrajong Heights (Speed limit 60 km/h) 79 km/h 64 km/h
Princes Hwy, Nth. Wollongong (Speed limit 60 km/h) 70 km/h 62 km/h
Newcastle Road, Lambton (Speed limit 70 km/h) 78 km/h 72 km/h
5.3 Results for Dummy Photo Radar Cameras
Dummy cameras have been shown to be not as effective as actual fixed photo radar
cameras. Though dummy cameras reduce the speed of vehicles in a statistical significance,
this change is not dramatic. Table 5.5 shows a comparison of the vehicle percentile that
did not exceed the speed limit before, immediately, and after the installation of fixed photo
radar cameras. When we make a ratio comparison on the averages of how effective fixed
radar cameras are in reducing speeds and how dummy cameras are, then immediately after
the installation of both fixed and dummy cameras it can be shown that they both have the
24


same affect after installation. On average, 68% of vehicles reduce the speeds immediately
after the installation when compared to before the installation of both fixed and dummy
cameras. However, in the long-term, the dummy cameras decrease their effectiveness in
controlling speeding vehicles, though they reduce the speed of vehicles when compared to
before the installation.
Table 5.5 Effectiveness of Dummy Photo Radar Cameras in Controlling Speed
of Vehicles that Do Not Exceed the Speed Limit
Speed Limit kph Percentile
Before Installation Immediately Installation After Installation
Road 1 80 40.25 73.17 59.68
Road 6 60 45.80 72.50 46.89
Road 8 80 67.40 84.89 70.90
Road 10 120 50.17 63.19 52.36
AVERAGE 50.91 73.44 57.46
In Table 5.6, the same data is compared for vehicles that did not exceed 10 km/h over the
speed limit. We can observe that on average about 95% of vehicles did not exceed 10 kph
immediately after the installation of the dummy photo radar cameras. However, in the
long-term, more vehicles start to speed again having on average of only about 87% of
vehicles not exceeding 10 kph over the speed limit. Though this still reduces the number
of speeding vehicles than before the installation of the dummy cameras, they are not nearly
as effective as the fixed photo radar cameras. It is possible that, in the long-term, drivers
who frequently use the road realise that the dummy cameras do not take photos of
violating vehicles. Drivers possibly know it by recognising that the flash does not go on
during the night when speeding drivers know they are violating the law, or when drivers
know they have violated the speeding law when they passed the dummy camera and realise
that they have not been fined. The main reason why more vehicles still reduce their speeds
25


is possibly caused by infrequent travellers who may have not, yet, realised that a dummy
camera is actually installed.
Table 5.6 Effectiveness of Dummy Photo Radar Cameras in Controlling Speed
of Vehicles that Do Not Exceed 10 km/h over the Speed Limit
10 kph Over Speed Limit Percentile
Before Installation Immediately Installation After Installation
Road 1 90 82.22 94.56 92.18
Road 6 70 74.10 91.27 83.33
Road 8 90 98.79 99.44 99.63
Road 10 130 66.32 93.98 74.35
AVERAGE 80.36 94.81 87.37
Table 5.7 records the 85th percentile speeds of vehicles that were monitored by dummy
photo radar camera installations that are used in this study. This table also shows further
that although immediately after the installation of the dummy cameras, many drivers
reduce their speeds. This speed is later resumed when evidendy frequent drivers realise
that no camera is actually taking photos of violating vehicles. Hence, knowing that fixed
photo radar cameras are expensive to install and maintain, then dummy cameras can only
be effective on roads that are not used by frequent drivers. Thus, it may be used in
highways that are travelled by infrequent drivers, such as in rural highways between cities.
Only then could it be possibly more effective than being used in urban or suburban roads
as an alternative to fixed photo radar cameras.
26


Table 5.7 85th Percentile Speeds on Roads with Dummy Photo Radar Cameras
Installed
Speed Limit kph 85th Percentile
Before kph Immediately kph After kph
Road 1 80 90 84 87
Road 6 60 73 67 70
Road 8 80 84 80 84
Road 10 120 145 126 141
5.4 Results for Signs Claiming the Existence of Fixed
Photo Radar Cameras
Signs that falsely claim the existence of fixed photo radar cameras seem to be as effective
as dummy cameras. When doing comparisons from Table 5.8, the before and after data fall
in the same trend as dummy cameras. 12% more vehicles reduce their speeds in a long
term after the installation in both dummy cameras and signs. Thus, we may also conclude
that frequent drivers realise that signs falsely claim the existence of the fixed cameras.
Frequent drivers probably try to identify the location of the fixed photo radar cameras, but
recognise their inexistence after a while. Nonetheless, fixed photo radar cameras, dummy
cameras, and signs seem to have the same effects immediately after their installations. On
average, each of them reduces the number of speeding vehicles by an extra 32% below the
speed limit.
27


Table 5.8 Effectiveness of Signs Falsely Claiming the Existence of Fixed Photo
Radat Cameras in Controlling Speed of Vehicles that Do Not Exceed the
Speed Limit
Speed Limit kph Percentile
Before Installation Immediately Installation After Installation
Road 3 80 49.57 70.48 63.68
Road 4 140 73.35 85.80 78.62
Road 5 110 64.13 72.00 70.64
AVERAGE 62.35 76.09 70.98
In Table 5.9, the same data is compared for vehicles that did not exceed 10 km/h over the
speed limit. We can observe that on average about 93% of vehicles did not exceed 10 kph
immediately after the installation of the signs. However, in the long-term, more vehicles
started to resume their speeds again having on average of only about 91% of vehicles not
exceeding 10 kph over the speed limit. Though this still reduces the number of speeding
vehicles than before the installation of the signs, they are similar to the dummy cameras by
not being nearly as effective as the fixed photo radar cameras. Like dummy cameras, it is
possible that, in the long-term, drivers who frequently use the road realise that the signs
falsely claim the existence of the fixed radar cameras. The data for this thesis is in parallel
with the conclusions made in a study by the Ontario Ministry of Transportation in 1994,
where the study has also found that signs, which falsely claim the existence photo radar
cameras are only effective for a very short time. The main reason why more vehicles still
reduce their speeds in the long term, when compared to before the installation of the signs,
is possibly caused by infrequent travellers who may have not, yet, realised that the signs
falsely claim the existence of the fixed photo radar cameras.
28


Table 5.9 Effectiveness of Signs Falsely Claiming the Existence of Fixed Photo
Radat Cameras in Controlling Speed of Vehicles that Do Not Exceed 10
km/h over the Speed Limit
10 kph Over Speed Limit Percentile
Before Installation Immediately Installation After Installation
Road 3 90 91.21 96.26 92.56
Road 4 150 86.97 96.70 94.03
Road 5 120 85.47 85.04 86.22
AVERAGE 87.88 92.67 90.94
Table 5.10 records the 85th percentile speeds of vehicles that were monitored by dummy
photo radar camera installations that are used in this study. This table also shows further
that although immediately after the installation of the dummy cameras, many drivers
reduce their speeds. This speed is later resumed when evidently frequent drivers realise
that no camera is actually taking photos of violating vehicles. Hence, knowing that fixed
photo radar cameras are expensive to install and maintain, then dummy cameras can only
be effective on roads that are not used by frequent drivers. Thus, it may be used in
highways that are travelled by infrequent drivers, such as in rural highways between cities.
Only then could it be possibly more effective than being used in urban or suburban roads
as an alternative to fixed photo radar cameras.
29


Table 5.10 85th Percentile Speeds on Roads with Signs Falsely Claiming the
Existence of Fixed Photo Radar Cameras Installed
Speed Limit kph 85th Percentile
Before kph Immediately kph After kph
Road 3 80 86 86 86
Road 4 140 148 139 143
Road 5 110 119 119 118
5.5 Conclusion and Recommendations
After analysing the three methods of speed control in this thesis (1) fixed photo
enforcement, (2) dummy photo cameras, and (3) signs falsely claiming the existence of
photo enforcement when they actually do not exist, it can be observed from the data
collected that fixed photo radar enforcement is the most effective mean to reduce speed.
This analysis agrees with other studies in literature that found great success in the usage of
photo radar cameras.
This study has also demonstrated that dummy photo radar cameras are only effective in
the short-term, though the average speeds in the long-term are still less than the average
speeds before their installation. This phenomenon is presumed to be caused by frequent
users of the road, who in the long-term, apprehend that a dummy photo radar camera is
placed, resulting in their resumption of prior speeds. However, it is also supposed that
infrequent travellers on the road might still imagine that an actual photo radar camera is
installed, and therefore reduce their speeds. This might be the basis on why dummy photo
radar cameras would still reduce average speeds in the long-term, but not as effective as
actual cameras.
Results for the usage of signs that falsely claim the existence of photo radar cameras are
similar to that of the usage of dummy cameras. Though in the short-term they may be
30


effective, they lose their efficacy in the long-term. This is also possibly due to the
recognition of frequent travellers of the false claim that the signs are making, while
infrequent travellers may still reduce their speeds, causing the average speeds to be still
slightly less than it was before the placement of the signs. A study in Australia has also
made a similar conclusion in a 1994 study (Ministry of Transportation of Ontario, 1997).
Due to the results and conclusions made in this thesis, it may be recommended to place
dummy cameras or false signs on rural roads or highways that may not be used by frequent
travellers. Most urban roads and highways are used by frequent travellers; thus, reducing
the usefulness of dummy cameras and false signs. In such cases, actual speed photo radar
enforcement may be necessary. Future studies are recommended to research how
combinations of different methods of traffic speed control can affect drivers behaviour.
Publicising the locations of speed cameras, by using true signs or the media, may allow
more efficacy as a study in Australia has suggested. However, more research in that matter
needs to be made to make a better conclusion.
31


APPENDIX
A. Summary Charts and Tables of Traffic Speed Data of
Monitored Roads
Chart A.1 Road 1 Histogram Before Data
Road 1 Histogram Before Data
't-OOCOCOOh-lOCMOi
Speed (kph)
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
ttf- .00%
SB Frequency
a Cumulative %
32


Chart A.2 Road 1 Histogram Immediately After Data
Road 1 Histogram Short-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
psssi Frequency
s Cumulative %

Speed (kph)
33


Chart A.3 Road 1 Histogram After Data
Road 1 Histogram Long-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
OS^-ONtJ t-OOLO
'tf^iococor'-oocoo)
r^n Frequency
Cumulative %
Speed (kph)
34


Table AT Road 1 Summary
Before Immediately After

Mean 80.226 Mean 72.205 Mean 75.063
Standard Error 0.209 Standard Error 0.249 Standard Error 0.229
Median 82 Median 74 Median 77
Mode 89 Mode 80 Mode 79
Standard Deviation 10.683 Standard Deviation 12.731 Standard Deviation 11.703
Sample Variance 114.133 Sample Variance 162.091 Sample Variance 136.966
Kurtosis 0.647 Kurtosis -0.296 Kurtosis -0.066
Skewness -0.710 Skewness -0.549 Skewness -0.617
Range 101.398 Range 86.315 Range 86.356
Minimum 10.602 Minimum 12.685 Minimum 11.644
Maximum 112 Maximum 99 Maximum 98
Sum 209630 Sum 188671 Sum 196141
Count 2613 Count 2613 Count 2613
Confidence Level(95.0%) 0.410 Confidence Level(95.0%) 0.488 Confidence Level(95.0%) 0.449


Chart A.4 Road 2 Histogram Before Data
Road 2 Histogram Before Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
oCT>cor--com''t
Ifi CO N 00 O)
Wxm Frequency
-a Cumulative %
Speed (kph)
36


Chart A.5 Road 2 Histogram Immediately After Data
Road 2 Histogram Short-Term Data
200
OCOCO'tf-CMOOOCO
'^-^Locor^-ooooa)
Speed (kph)
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
El Frequency
b Cumulative %
37


Chart A.6 Road 2 Histogram After Data
Road 2 Histogram Long-Term Data
200
120.00%
100.00%
80.00%
- 60.00%
- 40.00%
20.00%
.00%
Speed (kph)
Frequency
Cumulative %
38


Table A.2 Road 2 Summary
Before Immediately After

Mean 83.104 Mean 78.686 Mean 74.204
Standard Error 0.169 Standard Error 0.185 Standard Error 0.210
Median 84 Median 80 Median 76
Mode 80 Mode 82 Mode 79
Standard Deviation 8.608 Standard Deviation 9.417 Standard Deviation 10.655
Sample Variance 74.091 Sample Variance 88.685 Sample Variance 113.538
Kurtosis 0.504 Kurtosis 0.250 Kurtosis -0.118
Skewness -0.579 Skewness -0.606 Skewness -0.621
Range 60 Range 59 Range 55
Minimum 40 Minimum 40 Minimum 41
Maximum 100 Maximum 99 Maximum 96
Sum 214741 Sum 203325 Sum 191743
Count 2584 Count 2584 Count 2584
Confidence Level(95.0%) 0.332 Confidence Level(95.0%) 0.363 Confidence Level(95.0%) 0.411
39


Chart A. 7 Road 3 Histogram Before Data
Road 3 Histogram Before Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
T-NCOOHDT-SfOOilO
10IO(D(DSCOOOO)0)0
Frequency
-a Cumulative %
Speed (kph)
40


Chart A.8 Road 3 Histogram Immediately After Data
Road 3 Histogram Short-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
OCOCNCOtTOCOCMOO
lOLOtotDNcooomcn
EZD Frequency
-b Cumulative %
Speed (kph)
41


Chart A.9 Road 3 Histogram After Data
Road 3 Histogram Long-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
<& &K'-^
Speed (kph)
Frequency
Cumulative %
42


Table A.3 Road 3 Summary
Before Immediately After

Mean 80.474 Mean 76.035 Mean 76.439
Standard Error 0.156 Standard Error 0.210 Standard Error 0.208
Median 81 Median 77 Median 77
Mode 85 Mode 80 Mode 77
Standard Deviation 7.366 Standard Deviation 9.881 Standard Deviation 9.794
Sample Variance 54.261 Sample Variance 97.640 Sample Variance 95.929
Kurtosis 0.056 Kurtosis -0.234 Kurtosis -0.752
Skewness 0.040 Skewness -0.450 Skewness -0.052
Range 56 Range 51 Range 53
Minimum 51 Minimum 50 Minimum 50
Maximum 107 Maximum ' 101 Maximum 103
Sum 178571 Sum 168721 Sum 169618
Count 2219 Count 2219 Count 2219
Confidence Level(95.0%) 0.307 Confidence Level(95.0%) 0.411 Confidence Level(95.0%) 0.408
43


Chart A.10 Road 4 Histogram Before Data
Road 4 Histogram Before Data
in s a
SCO O)
co in s
C\J 00
05
lO
T- CO
S- CO
120.00%
100.00%
80.00%.
60.00%,
40.00%,
20.00%,
.00%
Frequency
-a Cumulative %>
Speed
44


Chart A.11 Road 4 Histogram Immediately After Data
Road 4 Histogram Short-Term Data
> 350 -
300 -

120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
B Frequency
- Cumulative %
OnCDffiCMlOODr-^S
SOCBOMCO^tDSOO
Speed (kph)
45


Chart A.12 Road 4 Histogram After Data
Road 4 Histogram Long-Term Data
600
500
$ 400
c
a)
3 300
XT
2
ul 200
100
0
tocoo CM^j- rn coocm
SOOOt-CMcO^-CDNOO
Speed (kph)
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
wm Frequency
-h Cumulative %
46


Table A.4 Road 4 Summary
Before Immediately After

Mean 130.903 Mean 127.288 Mean 128.307
Standard Error 0.152 Standard Error 0.135 Standard Error 0.143
Median 131 Median 129 Median 129
Mode 114 Mode 120 Mode 113
Standard Deviation 15.644 Standard Deviation 13.883 Standard Deviation 14.696
Sample Variance 244.730 Sample Variance 192.746 Sample Variance 215.960
Kurtosis -0.198 Kurtosis 0.087 Kurtosis -0.077
Skewness -0.108 Skewness -0.274 Skewness -0.194
Range 116 Range 118 Range 112
Minimum 75 Minimum 70 Minimum 76
Maximum 191 Maximum 188 Maximum 188
Sum 1379067 Sum 1340984 Sum 1351719
Count 10535 Count 10535 Count 10535
Confidence Level(95.0%) 0.299 Confidence Level(95.0%) 0.265 Confidence Level(95.0%) 0.281


Chart A.13 Road 5 Histogram Before Data
Road 5 Histogram Before Data
> 350
c 300
d)
S 250
S 200
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
r~-~i Frequency
-a Cumulative %
OJOJintOt-rJ-NOntD
scnoi-co^-ioscoo)
Speed (kph)
48


Chart A.14 Road 5 Histogram Immediately After Data
Road 5 Histogram Short-Term Data
600
c\jLnoo-<-'^-r^ococDCT)
i''-oocr>'t-cMcoiocor'-oo
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
ESE Frequency
-s Cumulative %
Speed (kph)
49


Chart A. 15 Road 5 Histogram After Data
Road 5 Histogram Long-Term Data
700
§ 400 3 1 s5 Jipn - 60.00% FEssi Frequency
300 - IKj -a Cumulative %
U. - 40.00%
120.00%
100.00%
80.00%
20.00%
.00%
OOQOT-NCOTfintON
Speed (kph)
50


Table A.5 Road 5 Summary
Before Immediately After

Mean 107.680 Mean 105.806 Mean 106.483
Standard Error 0.126 Standard Error 0.132 Standard Error 0.118
Median 106 Median 103 Median 104
Mode 92 Mode 101 Mode 96
Standard Deviation 12.047 Standard Deviation 12.638 Standard Deviation 11.263
Sample Variance 145.135 Sample Variance 159.710 Sample Variance 126.854
Kurtosis 2.450 Kurtosis 0.723 Kurtosis 0.303
Skewness 0.962 Skewness 0.870 Skewness 0.907
Range 121 Range 126 Range 93
Minimum 79 Minimum 72 Minimum 81
Maximum 200 Maximum 198 Maximum 174
Sum 983336 Sum 966224 Sum 972400
Count 9132 Count 9132 Count 9132
Confidence Level(95.0%) 0.247 Confidence Level(95.0%) 0.259 Confidence Level(95.0%) 0.231
51


Chart A.16 Road 6 Histogram Before Data
Road 6 Histogram Before Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
r-sroo)in-!-Noouo
cocoTt'^-iococor^r^-oo
r~^>] Frequency
-b Cumulative %
Speed (kph)
52


Chart Aul7 Road 6 Histogram Immediately After Data
Road 6 Histogram Short-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
CNCD'tO(DCNOO^O(D coeO'3-ioir>cD(or^-oooo £
EH3 Frequency
-b Cumulative %
Speed (kph)
53


Chart A.18 Road 6 Histogram After Data
Road 6 Histogram Long-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
T-scooLOT-snoiin
COCOrJ-Tj-inCDCONNCO
Speed
r^g Frequency
Cumulative %
54


Table A.6 Road 6 Summary
Before Immediately After

Mean 61.826 Mean 56.911 Mean 61.183
Standard Error 0.215 Standard Error 0.191 Standard Error 0.196
Median 62 Median 56 Median 61
Mode 47 Mode 54 Mode 65
Standard Deviation 10.478 Standard Deviation 9.320 Standard Deviation 9.542
Sample Variance 109.779 Sample Variance 86.856 Sample Variance 91.052
Kurtosis -1.118 Kurtosis 0.512 Kurtosis -0.550
Skewness 0.064 Skewness 0.679 Skewness -0.041
Range 58 Range 59 Range 58
Minimum 31 Minimum 32 Minimum 31
Maximum 89 Maximum 91 Maximum 89
Sum 147269 Sum 135561 Sum 145739
Count 2382 Count 2382 Count 2382
Confidence Level(95.0%) 0.421 Confidence Level(95.0%) 0.374 Confidence Level(95.0%) 0.383
55


Chart A.19 Road 7 Histogram Before Data
Road 7 Histogram Before Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
OCOCMOOxtOCDCMOOrt-
^^wiotDSNooooo)
l~^i Frequency
-b Cumulative %
Speed (kph)
56


Chart A.20 Road 7 Histogram Immediately After Data
Road 7 Histogram Short-Term Data
O CD CD CM O 00 CD
Mt^tlDCDr^-OOOOO)
Speed (kph)
Frequency
Cumulative %
57


Chart A.21 Road 7 Histogram After Data
Road 7 Histogram Long-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
t-snoiiOT-Nnoun
^^miotoNSooooo)
n^n Frequency
Cumulative %
Speed (kph)
58


Table A.7 Road 7 Summary
Before Immediately After

Mean 79.301 Mean 73.795 Mean 73.097
Standard Error 0.132 Standard Error 0.164 Standard Error 0.152
Median 80 Median. 74 Median 74
Mode 82 Mode 72 Mode 77
Standard Deviation 6.174 Standard Deviation 7.654 Standard Deviation 7.111
Sample Variance 38.113 Sample Variance 58.584 Sample Variance 50.572
Kurtosis 4.247 Kurtosis 0.445 Kurtosis 0.299
Skewness -1.137 Skewness -0.145 Skewness -0.203
Range 58 Range 58 Range 58
Minimum 40 Minimum 40 Minimum 41
Maximum 98 Maximum 98 Maximum 99
Sum 173113 Sum 161095 Sum 159571
Count 2183 Count 2183 Count 2183
Confidence Level(95.0%) 0.259 Confidence Level(95.0%) 0.321 Confidence Level(95.0%) 0.298
59


Chart A.22 Road 8 Histogram Before Data
Road 8 Histogram Before Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
OS'tT-OOlOCMOtDCO
^^inCOCDNCOOOOJO
Frequency
-a Cumulative %
Speed (kph)
60


Chart A.23 Road 8 Histogram Immediately After Data
Road 8 Histogram Short-Term Data
OCDWOS'tr-COWM OO-sJ-lOCDCDf'-COCOCDO £
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
Frequency
-a Cumulative %
Speed (kph)
61


Chart A.24 Road 8 Histogram After Data
Road 8 Histogram Long-Term Data
O0CMCO^OCDCS|OO't
^^loiocor^-h-oocoos
Frequency
-o Cumulative %
Speed (kph)
62


Table A.8 Road 8 Summary
Before Immediately After

Mean 74.823 Mean 74.252 Mean 75.111
Standard Error 0.210 Standard Error 0.155 Standard Error 0.201
Median 75 Median 75 Median 75
Mode 74 Mode 71 Mode 75
Standard Deviation 9.704 Standard Deviation 7.161 Standard Deviation 9.315
Sample Variance 94.173 Sample Variance 51.287 Sample Variance 86.763
Kurtosis 0.180 Kurtosis 1.621 Kurtosis 1.621
Skewness -0.398 Skewness -0.383 Skewness -0.969
Range 65 Range 69 Range 58
Minimum 40 Minimum 39 Minimum 40
Maximum 105 Maximum 108 Maximum 98
Sum 160420 Sum 159196 Sum 161039
Count 2144 Count 2144 Count 2144
Confidence Level(95.0%) 0.411 Confidence Level(95.0%) 0.303 Confidence Level(95.0%) 0.395
63


Chart A.25 Road 9 Histogram Before Data
Road 9 Histogram Before Data
120.00%
100.00%
80.00%
60.00%
40.00%
+ 20.00%
00%
OOCON-CDLO-cJ-COCMt-
SOOOCMTMOgOOCMTt
Frequency
-s Cumulative %
Speed (kph)
64


Chart A.26 Road 9 Histogram Immediately After Data
Road 9 Histogram Short-Term Data
600
500
> 400
c
S 300
O-
0)
i 200
100
0
120.00%
a) co h-
CD 00 O)
in O) CO N T- ID
CNI CO in CD 00 G
Speed (kph)
65


Chart A.27 Road 9 Histogram After Data
Road 9 Histogram Long-Term Data
600
500
- 80.00%
120.00%
100.00%
l#i] <|j|i - 60.00% Frequency
ill -a Cumulative %
ill £'^1 Hi - 40.00%
20.00%
.00%
OOOOOOOOOO r^oocnot-cNcoTj-iDto £
Speed (kph)
66


Table A.9 Road 9 Summary
Before Immediately After

Mean 119.152 Mean 110.194 Mean 106.818
Standard Error 0.208 Standard Error 0.144 Standard Error 0.132
Median 117 Median 112 Median 108
Mode 117 Mode 118 Mode 117
Standard Deviation 20.321 Standard Deviation 14.046 Standard Deviation 12.920
Sample Variance 412.931 Sample Variance 197.291 Sample Variance 166.933
Kudos is -0.325 Kudosis 0.106 Kudosis -0.083
Skewness 0.216 Skewness -0.133 Skewness -0.111
Range 181 Range 132 Range 99
Minimum 70 Minimum 69 Minimum 70
Maximum 251 Maximum 201 Maximum 169
Sum 1137906 Sum 1052350 Sum 1020108
Count 9550 Count 9550 Count 9550
Confidence Level(95.0%) 0.408 Confidence Level(95.0%) 0.282 Confidence Level(95.0%) 0.259


Chart A.28 Road 10 Histogram Before Data
Road 10 Histogram Before Data
400
350
300
o 250
0)
3 200
fl50
100
50
0
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
i-nifiso)T-comso)T-
sooroox-n^intDso)
Si Frequency
-a Cumulative %
Speed (kph)
68


Chart A.29 Road 10 Histogram Immediately After Data
Road 10 Histogram Short-Term Data
700
t-COIONCDt-COLOSCD
SCOO)Ot-P)^10(DN
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
CEZD Frequency
-b Cumulative %
Speed (kph)
69


Chart A.30 Road 10 Histogram After Data
Road 10 Histogram Long-Term Data
120.00%
.00%
toinscaT-ninNOT-
Ncoffiorgco^iocooo
r~l Frequency
-b Cumulative %
72
70


Table A.10 Road 10 Summary
Before Immediately After

Mean 122.829 Mean 116.223 Mean 122.221
Standard Error 0.189 Standard Error 0.106 Standard Error 0.166
Median 120 Median 118 Median 120
Mode 117 Mode 118 Mode. 121
Standard Deviation 18.758 Standard Deviation 10.524 Standard Deviation 16.466
Sample Variance 351.852 Sample Variance 110.748 Sample Variance 271.142
Kurtosis -0.669 Kurtosis 1.137 Kurtosis 0.116
Skewness 0.338 Skewness -0.316 Skewness 0.520
Range 120 Range 112 Range 117
Minimum 71 Minimum 71 Minimum 73
Maximum 191 Maximum 183 Maximum 190
Sum 1205072 Sum 1140259 Sum 1199112
Count 9811 Count 9811 Count 9811
Confidence Level(95.0%) 0.371 Confidence Level(95.0%) 0.208 Confidence Level(95.0%) 0.326
71


Chart A.31 Road 11 Histogram Before Data
Road 11 Histogram Before Data
450
400
350
> 300
o
S 250
t 200
£ 150
100
50
0
OOi-M-SOCO(DC35Mlf)
CDCOOJOCNIO^-inSCO
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
r~l Frequency
-a Cumulative %
Speed (kph)
72


Chart A.32 Road 11 Histogram Immediately After Data
Road 11 Histogram Short-Term Data
oocooicgiooof'ts
soorootMco^toNoo
FP^I Frequency
-a Cumulative %
Speed (kph)
73


Chart A.33 Road 11 Histogtam After Data
Road 11 Histogram Long-Term Data
psi Frequency
Cumulative %
Speed (kph)
74


Table A.11 Road 11 Summaty
Before Immediately After

Mean 124.060 Mean 119.146 Mean 113.213
Standard Error 0.212 Standard Error 0.151 Standard Error 0.100
Median 121 Median 118 Median 116
Mode 120 Mode 117 Mode 117
Standard Deviation 21.023 Standard Deviation 14.995 Standard Deviation 9.934
Sample Variance 441.983 Sample Variance 224.842 Sample Variance 98.693
Kurtosis -0.203 Kurtosis 1.831 Kurtosis 5.868
Skewness 0.345 Skewness 0.683 Skewness -0.443
Range 124 Range 120 Range 130
Minimum 68 Minimum 70 Minimum 71
Maximum 192 Maximum 190 Maximum 201
Sum 1216041 Sum 1167871 Sum 1109710
Count 9802 Count 9802 Count 9802
Confidence Level(95.0%) 0.416 Confidence Level(95.0%) 0.297 Confidence Level(95.0%) 0.197
75


Chart A.34 Road 12 Histogram Before Data
Road 12 Histogram Before Data
350
120.00%
i-tSOnCDOlCMlflCO
sooroT-c^n^tosoo
mu Frequency
-a Cumulative %
Speed (kph)
76


Chart A.35 Road 12 Histogram Immediately After Data
Road 12 Histogram Short-Term Data
450
400
350
> 300
§ 250
| 200
£ 150
100
50
0

120.00%
100.00%
Ml Frequency
Cumulative %
CMTt-(DCOOCM^(DCOO
SCOO)0(MCO'tm(D(X)
Speed (kph)
77


Chart A.36 Road 12 Histogram After Data
Road 12 Histogram Long-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
OHDnONTj-^-CO
mh-O-i-CSI'st-COh'.S
(HU Frequency
Cumulative %
Speed (kph)
78


Table A.12 Road 12 Summary
Before Immediately After

Mean 125.866 Mean 120.766 Mean 109.294
Standard Error 0.211 Standard Error 0.176 Standard Error 0.157
Median 123 Median 120 Median 109
Mode 125 Mode 137 Mode 112
Standard Deviation 21.708 Standard Deviation 18.142 Standard Deviation 16.138
Sample Variance 471.245 Sample Variance 329.144 Sample Variance 260.433
Kurtosis -0.823 Kurtosis -0.211 Kurtosis 2.179
Skewness 0.254 Skewness 0.087 Skewness 0.999
Range 127 Range 111 Range 135
Minimum 71 Minimum 72 Minimum 59
Maximum 198 Maximum 183 Maximum 194
Sum 1331409 Sum 1277458 Sum 1156116
Count 10578 Count 10578 Count 10578
Confidence Level(95.0%) 0.414 Confidence Level(95.0%) 0.346 Confidence Level(95.0%) 0.308


Chart A.37 Road 13 Histogram Before Data
Road 13 Histogram Before Data
600
0 *tiS&
120.00%
100.00%
tTNOPXDO)
s oo o) cm co in co Is- co
nna Frequency
-a Cumulative %
Speed (kph)
80


Chart A.38 Road 13 Histogram Immediately After Data
Road 13 Histogram Short-Term Data
100
0
N05T-nwso5i-nifls
(DSOlO^-MnUJlDSOO
F^l Frequency
Cumulative %
Speed (kph)
81


Chart A.39 Road 13 Histogram After Data
Road 13 Histogram Long-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
lO(DSO)OCMtO^(DSO)
Frequency
s Cumulative %
Speed (kph)
82


Table A.13 Road 13 Summary
Before Immediately After

Mean 119.962 Mean 109.575 Mean 98.109
Standard Error 0.184 Standard Error 0.111 Standard Error 0.117
Median 122 Median 108 Median 99
Mode 123 Mode 108 Mode 99
Standard Deviation 19.274 Standard Deviation 11.659 Standard Deviation 12.325
Sample Variance 371.483 Sample Variance 135.942 Sample Variance 151.915
Kurtosis -0.384 Kurtosis 3.375 Kurtosis 3.760
Skewness 0.014 Skewness 0.815 Skewness 0.325
Range 127 Range 130 Range 147
Minimum 72 Minimum 67 Minimum 51
Maximum 199 Maximum 197 Maximum 198
Sum 1322216 Sum 1207732 Sum 1081360
Count 11022 Count 11022 Count 11022
Confidence Level(95.0%) 0.360 Confidence Level(95.0%) 0.218 Confidence Level(95.0%) 0.230
83


Chart A.40 Road 14 Histogram Before Data
Road 14 Histogram Before Data
Frequency
Cumulative %
Speed (kph)
84


Chart A.41 Road 14 Histogram Immediately After Data
Road 14 Histogram Short-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
msi Frequency
- Cumulative %
Speed (kph)
85


Chart A.42 Road 14 Histogram After Data
Road 14 Histogram Long-Term Data
Speed (kph)
F^l Frequency
-a Cumulative %
86


Table A.14 Road 14 Summary
Before Immediately After

Mean 79.592 Mean 75.095 Mean 73.332
Standard Error 0.197 Standard Error 0.179 Standard Error 0.182
Median 81 Median 77 Median 76
Mode 85 Mode 77 Mode 76
Standard Deviation 10.835 Standard Deviation 9.854 Standard Deviation 10.042
Sample Variance 117.389 Sample Variance 97.094 Sample Variance 100.852
Kurtosis 1.981 Kurtosis 1.514 Kurtosis 0.608
Skewness -0.964 Skewness -0.844 Skewness -0.755
Range 76 Range 66 Range 64
Minimum 41 Minimum 40 Minimum 40
Maximum 117 Maximum 106 Maximum 104
Sum 241164 Sum 227538 Sum 222196
Count 3030 Count 3030 Count 3030
Confidence Level(95.0%) 0.386 Confidence Level(95.0%) 0.351 Confidence Level(95.0%) 0.358


Chart A.43 Road 15 Histogram Before Data
Road 15 Histogram Before Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
Frequency
Cumulative %
i-ooioMroconos^-
't^in(D(DS00050)0
Speed (kph)
88


Chart A.44 Road 15 Histogram Immediately After Data
Road 15 Histogram Short-Term Data
r^i Frequency
-h Cumulative %
Speed (kph)
89


Chart A-45 Road 15 Histogram After Data
Road 15 Histogram Long-Term Data
120.00%
100.00%
80.00%
60.00%
40.00%
20.00%
.00%
ON^T-OOlO(MO)tO
^'tlOtDCDSCOOOO)
Speed (kph)
EHB Frequency
-o Cumulative %
90


Full Text

PAGE 1

TRAFFIC SPEED CONTROL USING SPEED CAMERAS by Abdulla Ibrahim Galadari B.S., University of Colorado, 2002 A thesis submitted to the University of Colorado at Denver in partial fulfilment of the requirements for the degree of Master of Science Civil Engineering 2003

PAGE 2

Tills thesis for the Master of Science degree by Abdulla Ibrahim Galadari has been approved by ruceJanson Guo t:'L}Tflil 12 L2 J.zoo:3 Date

PAGE 3

Galadari, Abdulla Ibrahim (M.S., Civil Engineering) Traffic Speed Control Using Speed Cameras Thesis directed by Professor Bruce Janson ABSTRACT This thesis compares the effects of the placement of three types of speed controllers, actual photo radar cameras, dummy casings of photo radar cameras, and signs that falsely claim the existence of photo radar cameras on drivers' behaviour in speeding. The speed data was obtained in three different timelines, before the placement of any of the controllers, within two weeks immediately after placement, and at least one year from the placement. A comparative study was made to see the effective change in the overall speeds of traffic due to the controllers. The study has found that in the short-term, all types of controllers have reduced speeds to some extent, though differently from each other. However, in the long-term, actual photo radar cameras have reduced speeds even more effectively, while speeds enforced by other controllers become less effective. This abstract accurately represents the content of the candidate's thesis. I recommend its publication. Bruce Janson 111

PAGE 4

DEDICATION This thesis is dedicated to everything and everyone, whether existing or not.

PAGE 5

ACKNOWLEDGMENTS I am sincerely grateful and thankful to the .Almlghty Creator of the Worlds. Tiu:ough the wisdom of un-wisdom will the Word of God be suppressed, for the Word of God is Wisdom, and only the fools will detest. "Read! In the Name of your Lord, Who has created. Has created the human from a clot of Read! And your Lord is the Most Generous. Who has taught by the pen. Who has taught the human that which he knew not." (Holy Qur'an, 96: 1 -5) "For the Lord giveth wisdom, out of His mouth cometh knowledge and discernment; He layeth up sound wisdom for the upright, He is a shield to them that walk in integrity; That He may guard the paths of justice, and preserve the way of His godly ones. Then shalt thou understand righteousness and justice, and equity, yea, every good path. For wisdom shall enter into thy heart, and knowledge shall be pleasant unto thy soul." (Mishlei [Proverbs], 2: 6 -10, Tanakh [Old Testament])

PAGE 6

TABLE OF CONTENTS List of Charts ................................................................................................................. vii List ofTables ................................................................................................................. .ix Chapter 1. Introduction .................................................................................................................. I 1.1 Topic and Overview .................................................................................................. 2. Literature Review ........................................................................................................ 7 2.1 History of Automated Traffic Enforcement ............................................................. 7 2.2 Worldwide Reports on the Effectiveness of Automated Traffic Enforcement ....... 8 3. Speed Enforcement .... ; ............................... ................................................................. 12 3.1 Speed Enforcement Issues ....................................................................................... 12 3.2 Necessary Factors for Alternative Speed Enforcement .......................................... l3 3.3 Factors that May Hinder the Use of Photo Radar Cameras ................................... 15 4. Methodology .............................................................................................................. 18 4.1 Collecting Data ........................................................................................................ 18 4.2 Data Analyses .......................................................................................................... 19 5. Speed Data Analysis and Results .... ......................................................................... 20 5.1 Statistical Analysis .................................................................................................. 20 5.2 Results for Fixed Photo Radar Cameras ................................................................. 20 5.3 Results for Dummy Photo Radar Cameras ............................................................. 24 5.4 Results for Signs Claiming the Existence of Fixed Photo Radar Cameras ........... 27 5.5 Conclusion and Recommendations ......................................................................... 30 Appendix A. Summary Charts and Tables of Traffic Speed Data of Monitored Roads .............. 32 Bibliography ......................................................................................... 92 Vl

PAGE 7

LIST OF CHARTS ChartA.1 Road 1 Histogram Before Data ............................................................................... .-32 Chart A.2 Road 1 Histogram Immediately After Data ........................................................... 33 Chart A.3 Road 1 Histogram After Data ................................................................................... 34 Chart A.4 Road 2 Histogram Before Data ................................................................................ 36 Chart A.5 Road 2 Histogram Immediately After Data ........................................................... 3 7 Chart A.6 Road 2 Histogram After Data ................................................................................... 38 Chart A. 7 Road 3 Histogram Before Data ................................................................................ 40 Chart A.8 Road 3 Histogram Immediately After Data ........................................................... 41 Chart A.9 Road 3 Histogram After Data ................................................................................... 42 Chart A.1 0 Road 4 Histogram Before Data .............................................................................. 44 Chart A.11 Road 4 Histogram Immediately After Data ......................................................... 45 Chart A.12 Road 4 Histogram After Data ................................................................................ 46 Chart A.13 Road 5 Histogram Before Data .............................................................................. 48 Chart A.14 Road 5 Histogram Immediately After Data ............. ........................................... 49 Chart A.15 Road 5 Histogram After Data ................................ : ............................................... 50 ChartA.16 Road 6 Histogram Before Data .............................................................................. 52 ChartA.17 Road 6 Histogram Immediately After Data ......................................................... 53 Chart A.18 Road 6 Histogram After Data ................................................................................ 54 Chart A.19 Road 7 Histogram Before Data .............................................................................. 56 Chart A.20 Road 7 Histogram Immediately After Data ......................................................... 57 Chart A.21 Road 7 Histogram After Data ............. .................................................................. 58 Chart A.22 Road 8 Histogram Before Data .................. ........................................................... 60 Chart A.23 Road 8 Histogram Immediately After Data ......................................................... 61 Chart A.24 Road 8 Histogram After Data ................................................................................ 62 Chart A.25 Road 9 Histogram Before Data .............................................................................. 64 Chart A.26 Road 9 Histogram Immediately After Data ......................................................... 65 Chart A.27 Road 9 Histogram After Data ................................................................................ 66 Chart A.28 Road 10 Histogram Before Data ........................................................................... 68 Chart A.29 Road 10 Histogram Immediately After Data ....................................................... 69 Chart A.30 Road 10 Histogram After Data ................................................... : .......................... 70 Chart A.31 Road 11 Histogram Before Data ........................................................................... 72 Chart A.32 Road 11 Histogram Immediately After Data ....................................................... 73 Chart A.33 Road 11 Histogram After Data .............................................................................. 7 4 Chart A.34 Road 12 Histogram Before Data ....................................................... : ................... 76 Chart A.35 Road 12 Histogram Immediately After Data ............................................. ......... 77 V11

PAGE 8

Chart A.36 Road 12 Histogram After Data .............................................................................. 78 Chart A.37 Road 13 Histogram Before Data ........................................................................... 80 Chart A.38 Road 13 Histogram Immediately After Data ....................................................... 81 Chart A.39 Road 13 Histogram After Data .............................................................................. 82 Chart A.40 Road 14 Histogram Before Data ................................. ; ......................................... 84 ChartA.41 Road 14 Histogram Immediately After Data ....................................................... 85 Chart A.42 Road 14 Histogram After Data .............................................................................. 86 Chart A.43 Road 15 Histogram Before Data ........................................................................... 88 Chart A.44 Road 15 Histogram Immediately After Data ....................................................... 89 Chart A.45 Road 15 Histogram After Data ........................................... .................................. 90 V111

PAGE 9

LIST OF TABLES Table 1.1 Major Causes of Death in Developed Regions ........................................................ Table 1.2 Major Causes of Deaths in Developing Regions ..................................................... 2 Table 1.3 Major Causes of Death in the United States ............................................................. 3 Table 5.1 Effectiveness of FiXed Photo Radar Cameras in Controlling Speed of Vehicles that Do Not Exceed the Speed Lirnit.. .................................................... 21 Table 5.2 Effectiveness of Fixed Photo Radar Cameras in Controlling Speed of Vehicles that Do Not Exceed 10 km/h over the Speed Lirnit ........................... Table 5.3 85th Speeds on Roads Monitored by Fixed Photo Radar Cameras .......................................................................................................................... 23 Table 5.4 85th Percentile Speeds on Roads Monitored by Fixed Photo Radar Catneras in Australia .................................................................................................... 24 Table 5.5 Effectiveness of Dwnmy Photo Radar Cameras in Controlling Speed ofVehicles that Do Not Exceed the Speed Lirnit.. ............................................... 25 Table 5.6 Effectiveness of Dwnmy Photo Radar Cameras in Controlling Speed ofVehicles that Do Not Exceed 10 km/h over the Speed Lirnit ....................... 26 Table 5.7 85th Percentile Speeds on Roads with Dummy Photo Radar Cameras Installed .......................................................................................................................... 27 Table 5.8 Effectiveness of Signs Falsely Claiming the Existence of Fixed Photo Radar Cameras in Controlling Speed of Vehicles that Do Not Exceed the Speed Limit .............................................................................................. 28 Table 5.9 Effectiveness of Signs Falsely Claiming the Existence of Fixed Photo Radar Cameras in Controlling Speed of Vehicles that Do Not Exceed 10 km/h over the Speed Lirnit .................................................................... 29 Table 5.10 85th Percentile Speeds on Roads with Signs Falsely Claiming the Existence of Fixed Photo Radar Cameras Installed ........................................... 30 Table A.1 Road 1 Summary ......................................................................................................... 35 Table A.2 Road 2 Summary ......................................................................................................... 39 Table A.3 Road 3 Summary ......................................................................................................... 43 Table A.4 Road 4 Summary ......................................................................................................... 47 Table A.5 Road 5 Summary ......................................................................................................... 51 Table A.6 Road 6 Summary ......................................................................................................... 55 Table A.7 Road 7 Summary ................................... ..................................................................... 59 Table A.8 Road 8 Summary ......................................................................................................... 63 Table A.9 Road 9 Summary ......................................................................................................... 67 Table A.1 0 Road 10 Summary ..................................................................................................... 71 1X

PAGE 10

Table A.11 Road 11 Swnmary ..................................................................................................... 75 Table A.12 Road 12 Swnmary ...................................................................................... ; .............. 79 Table A.13 Road 13 Summary ..................................................................................................... 83 Table A.14 Road 14 Swnmary ..................................................................................................... 87 Table A.15 Road 15 Summary ..................................................................................................... 91 X

PAGE 11

1. Introduction 1.1 Topic and Overview As technology of mass production increases, more people have access to vehicles. With the increase of the volume of vehicles in the str.eets of any metropolitan city or a town, more traffic accidents are expected. In many countries, traffic accidents are considered among the top ten main causes of death. In some developing countries, the usage of motor vehicles per capita is very limited while diseases, many of which are treatable, such as malaria, are the major causes of death. Nevertheless, traffic accidents cause about 2% of all deaths for all ages in both developed and developing countries. Tables 1.1 and 1.2, from the World Health Organisation, show worldwide statistics comparing the ten major causes of death in developed and developing countries, illustrating that traffic accidents cause about 2% of all deaths. Table 1.1 Major Causes of Death in Developed Regions %of Developed Regions Deaths 1 lschaemic heart disease 24.7 2 Cerebrovascular disease 13.1 3 Trachea, bronchus, and lung cancer 4.8 4 Lower respiratory infections 3.5 Chronic obstructive pulmonary 5 disease 3 6 Colon and rectum cancers 2.5 7 Stomach cancer 2.2 8 Traffic accidents 2 9 Suicide 1.8 10 Diabetes mellitus 1.6 1

PAGE 12

Table 1.2 Major Causes of Deaths in Developing Regions %of Developing Regions Deaths 1 Lower respiratory infections 9.9 2 lschaemic heart disease 9 3 Cerebrovascular disease 7.5 4 Diarrhoeal diseases 7.4 5 Conditions during prenatal period 4.9 6 Tuberculosis 4.7 7 Chronic obstructive pulmonary disease 2.7 8 Measles 2.6 9 Malaria 2.2 10 Traffic accidents 1.9 In the United States, traffic accidents have been in the top major causes of death, though deaths due to traffic accidents have decreased from 1960 to 2000 as seen in Table 1.3. In many countries, including the United States and Western Europe, traffic accidents are the leading cause of death of children and young adults. 2

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Table 1.3 Major Causes ofDeath in the United States Heart Cerebro-Chronic disease vascular lower diseases respiratory Year Cancer diseases Accidents 1970 492.7 198.6 147.7 21.3 62.2 1971 492.9 199.3 147.6 21.8 60.3 1972 490.2 200.3 147.3 22.8 60.2 1973 482 200 145.2 23.6 59.3 1974 458.8 201.5 136.8 23.2 52.7 1975 431.2 200.1 123.5 23.7 50.8 1976 426.9 202.5 117.4 24.9 48.7 1977 413.7 203.5 110.4 24.7 48.8 1978 409.9 204.9 103.7 26.3 48.9 1979 401.6 204 97.1 25.5 46.5 1980 412.1 207.9 96.4 28.3 46.4 1981 397 206.4 89.5 29 43.4 1982 389 208.3 84.2 2.1 40.1 1983 388.9 209.1 81.2 31.6 39.1 1984 378.8 210.8 78.7 32.4 39.8 1985 .375 211.3 76.6 34.5 38.5 1986 365.1 211.5 73.1 34.8 38.6 1987 355.9 211.7 71.6 35 38.2 1988 352.5 212.5 70.6 36.5 38.9 1989 332 214.2 66.9 36.6 37.7 1990 321.8 216 65.5 36.3 37.2 1991 313.8 215.8 63.2 38 34.9 1992 306.1 214.3 62 37.9 33.4 1993 309.9 214.6 63.1 40.9 34.5 1994 299.7 213.1 63.1 40.6 34.6 1995 296.3 211.7 63.9 40.5 34.9. 1996 288.3 208.7 63.2 41 34.9 1991 280.4 205.7 61.8 41.5 34.8 1998 272.4 202.4 59.6 42 35 1999 267.8 202.7 61.8 45.8 35.9 2000 257.5 200.5 60.2 44.9 33.9 Source: U.S. National Center for Health Statistics, Vital Statistics of the United States, annttaL From Statistical Abstract of the United Stales: 2002. 3

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Traffic accidents are inevitable. They transpire for many different reasons. Some reasons entail surface conditions, design, planning, construction, and operation. However, human error is one of the major causes of most accidents and it is the deadliest of them all. This entails poor judgement, lack of driving skills, failure to interact to existing roadway conditions, aggressive driving, driving under the influence of narcotics or alcohol, and unawareness of traffic rules. Speeding is one of the main causes of traffic accidents resulted by human error by not adhering to traffic rules of speed limits (Cowley, 1980, 1983, 1987; Dublin, 1982; .Sanderson and Cameron, 1982; Social Development Committee, 1991); the higher the speed of a vehicle, the greater the risk of an accident (Insurance Institute of Highway Safety, 1998). Speeding is the deadliest cause of accidents since the forces experienced by the human body in a collision increase exponentially as the speed increases. According .to the laws of physics, speed and energy dissipation in a .crash are directly related: for a given vehicle mass, the kinetic energy to be dissipated in a crash increases by the square of the impact speed: that is, if speed is doubled, four times the energy will be absorbed in the crash. For this reason, it is said that increased speed leads to increased crash severity, and probably increased injury severity to the vehicle occupants and a higher risk of fatalities . Speeding is a deliberate and calculated behaviour where the driver knows the risk but ignores the danger. Many jurisdictions have tried to reduce the number of speeding cars by enforcing harsh speeding laws. Different methods are used by different cities. This thesis focuses on three different methods and identifies which of them would be the most effective to control traffic speeds along streets and freeways. The three methods include (1) fixed photo enforcement, (2) dummy photo cameras, and (3) signs falsely clainnng the existence of photo enforcement when they actually do not exist. 4

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Fixed photo enforcement is an al)tomated enforcement system defined as a technical recording device triggered automatically by a speeding violation so that information about the violating vehicle is recorded, making possible the subsequent identification of the vehicle for purpose of sanctioning the owner or the driver. Speed cameras are developed to detect and photograph speeding vehicles. Slant radar measures the speeds of passing vehicles, while a camera control unit provides photographic evidence of the vehicle at the scene of the offence and records the time, date, location, and travelling speed. Speed cameras can be set so that both receding and/ or approaching vehicles can be monitored. The cameras are capable of taking two photographs per second and can be operated at any time of the day (or night). Thus, it can be presumed that the cameras are effective to identify most, if not all, vehicles that violate the speed I.a:ws on a specific portion of the road. Many countries have used photo radar speed enforcement for many years. The system has been found to be very effective in reducing traffic. speeds. Several world reports on the effectiveness of photo radar are discussed in this thesis to ensure the similarity between the data used for this thesis and the international efficacy of the system. The thesis tries to recognize a trend in drivers' behaviour, if any, that would effectively reduce the number of speeding vehicles to enforce the speed limits along a street or freeway using the three mentioned methods. Cameron et al. (1992) made a study in Australia identifying a covariance between speeding and alcohol consumption. The same study also found a covariance between unemployment rates and alcohol consumption. The study showed that motorists who drive under the influence of alcohol are less to be affected by speed cameras. Knowmg that alcohol levels in the blood can affect drivers' behaviour in regards to speed laws, the data used for this thesis is taken in parts of a city in which alcohol consumption is extremely limited and a societal taboo. Hence, it is safe to conclude that most drivers' monitored are not under the influence of and 5

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therefore, mentally aware of the speed laws along the paths they are driving. However, such an assumption would not be considered for the purposes of this thesis. 6

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2. Literature Review 2.1 History of Automated Traffic Enforcement Many studies worldwide have been made in regards to the effects of photo enforcement of speed limits after their installation. However, not many researches has been made to compare the other methoqs discussed in this thesis by having only signs that falsely claim the existence of photo radars or having dummy photo radar cameras. Some research has found that speeding is the main cause of all accidents, which is the source of the largest costs in the loss of life, injuries, and property damage (Taylor, Lynam and Baruya, 2000; Taylor, 2001; Taylor, Baruya and Kennedy, 2002). The first example of automatic traffic control reported in the research literature (Lamm and Kloeckner, 1984) was the photo-radar on Autobahn A3 between Cologne and Frankfurt installed in May 1973. The system included three radar devices (one per lane) mounted on a traffic sign bridge. on a downgrade section with a very high accident rate. The speed limits were 40 km/h for the right lane and 1 00 km/h for the other two. If the speeds exceeded 45 km/h for the right lane and 110 km/h for the other two lanes, a picture was automatically taken from the rear to fine the vehicles. In darkness, a flashlight was used. The authors report that in 1982, after almost 10 years from operation, only 10% object to the fine and that accident rates have been reported to have decreased effectively. By 1997, according to a review by Glauz (1998) about_75 countries world-wide have used photo radar for speed enforcement. Over the past couple of decades, automatic traffic enforcement has been used extensively for speeding alone or for red light violations and speeding at the same time. As of April 1996 there were 593 units in operation in Germany (Glauz, 1998). The United Kingdom uses this technology to a great length. Speed control by automated enforcement was tried out first at Twickenham bridge in 1990 and became fully operational in the London area in 1992 (Toogood, 1993). According to Zaidel and 7

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Makinen (1999) there wei:e 520 automated enforcement cameras in London, divided about equally between red light and speed enforcement. This alone makes up about one-fourth of all automated enforcement cameras in the United Kingdom. The United Kingdom also uses this technology to enforce bus-lane violations and for other different reasons. Many countries including S'\vitzerland, United States, Australia, Norway, Sweden, Canada, Netherlands, Finland, Austria, Denmark, Ireland, Kuwait, United Arab Emirates, and New Zealand have used automatic enforcement systems to detect traffic violators (Blackburn and Gilbert, 1995; Coleman et al., 1996; Glauz, 1998; Fildes, 1995; Glad and 0stvik, 1991; Nilsson, 1992). 2.2 Worldwide Reports on the Effectiveness of Automated Traffic Enforcement Different studies have been made in different parts of the world to identify the effectiveness of photo radar enforcement for detecting speed violators. In 1997, about 25 million vehicle speed measurements were made in Australia. About 2.3% of all the vehicles were speeding in areas where photo radar enforcements were installed. This was a reduction from about 24% at the start of the installations in 1989. Researchers at the Insurance Institute of Highway Safety (IIHS) measured travel speeds on seven D.C. streets before photo. radar cameras were installed and six months after their deployment (Insurance Institute of Highway Safety, 2002). It has been shown that the number of drivers travelling more than 1 0 mph over the speed limit dropped dramatically at every site, with reductions ranging from 38 to 89 percent. Statistics from the D.C. police department show that the percentage of vehicles aggressively speeding on D.C. streets and highways has declined by more than 58% since the photo radar program started in July 2001 to about a year later. When the photo radar was first deployed, it had a warning period for the first month. During this first month, 31% of the vehicles monitored by photo radar exceeded the program's speeding threshold. After 9 months since the 8

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deployment, only 13% of vehicles monitored by the radar have exceeded this threshold. According to the Insurance Institute of Highway Safety, speeding is a major factor in motor vehicle crashes, which are the leading cause of death for people under 34 years of age in the Ynited States. Photo radar enforcement ill the United States is only limited to about a dozen communities due to more complex laws. Nevertheless, in 1995, a nationwide telephone survey found that 57% of US residents favour using cameras to enforce speed limit laws (Insurance Institute of Highway Safety, 1998). Another study by the Insurance Institute of Highway Safety in British Columbia, Canada, shows a 7% decline in crashes and up to 20% fewer deaths were recorded in the first year of the deployment of the photo radar cameras. The proportion of speeding vehicles at photo radar deployment stations in British Columbia declined from 66% in 1996 to less than 40% in 1998. Researchers at the Insurance Institute of Highway Safety also attribute a 10% decline in daytime injuries to the photo radar enforcement. Extensive use of 54 photo radar cameras is credited with reducing speeding from 23% of all vehicles to just 3.8% of all vehicles in Victoria State, Australia. Researches in British Columbia, Australia, and Texas established an association between the use of photo radar and speed reduction. A 1988 Victoria, B.C. study discovered that photo radar cameras reduced speeds at study sites. Data from Victoria, Australia that was collected over two years showed that speed reduction at camera sites was greater when media publicity and signs announced the presence of photo radar. In Vancouver, B.C. research from a short-term 1994 study indicated that fewer vehicles travelled over the speed limit when photo radar was in place (Ministry of Transportation of Ontario, 1997). A research in Australia shows that immediately after the introduction of photo radar cameras, drivers on roads where the cameras were not installed dramatically reduced their speeds. Researchers attribute this drop to drivers' psychology for their fear that since other 9

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roads had photo radar cameras installed that roads in the surrounding area has also been introduced to the system, though that was not actually true. About six months since the implementation, drivers in surrounding roads resumed their speeds. However, about a year since the implementation, drivers' reduced their speeds again in fear that the cameras are being installed. Although the study shows that number of speeding vehicles on roads that actually had cameras installed was less by about 5% from the overall volunie than the surrounding roads, surrounding roads still showed a significant decrease in the number of drivers' speeding over the posted speed limit. At camera sites, about 27% of vehicles were over the speed limit compared to 40% before the camera installations. On surrounding roads, about 32% of vehicles were reported to be travelling over the speed limit compared to 40% before the cameras were installed in nearby roads (ARRB Transport Research, 2000, 2001). A study of photo radar on a German autobahn to increase compliance with a 100 km/h limit in the late 1970s reported increased compliance with the .limit and resulted in a reduction from 300 crashes, 80 injuries, and 7 fatalities to 9 crashes, 5 injuries, and no deaths (Blackburn & Glanz, 1984; cited in Freedman, Williams and Lund, 1990). Some researchers advocate the use of photo radar cameras extensively to be more effective to reduce the overall drivers' speed. Some studies in Arlington, Texas concluded that the presence of photo radar cameras reduced speeding; the greater the concentration of cameras, the greater the reduction in speeders (Ministry of Transportation of Ontario, 1997). Sweden, Germany, and Australia reported decreases in injury-producing collisions with the introduction of photo radar. During a 1990-to-1992 Swedish research project, data showed fewer injury-producing crashes both on test roadway sections monitored by cameras and on control sections of roadways not monitored by cameras. The reductions were greater, however, where there were cameras. German statistics compared collisions on the Autobahn in 1977, without photo radar, and in 1978, after the installation of photo radar. Researchers reported increased compliance with speed limits. Moreover, there were 10

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only 9 crashes, 7 injuries, and no deaths in 1978 compared with 300 crashes, 80 injuries, and 7 deaths the year before. Similarly, Australian statistics from 1992 and 1993 showed photo radar reduced injury-producing collisions on some roadways by as much as 20 percent. Several studies by the Ontario Ministry of Transportation have also concluded that photo radar cameras are more effective with media publicity, including the use of signs. The next chapter shows the reports from those studies as it may be useful to compare with studies' usage of signs falsely claiming the existence of photo radar cameras with the data being reported by this thesis. 11

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3. Speed Enforcement 3.1 Speed Enforcement Issues Excessive speed has long been recognised as a major factor in road crashes, and has led to great research into the factors which contribute to drivers' speeding behaviour (Fildes and Lee, 1993; French et al., 1993; Gregersen and Bjurulf, 1996; Harrison et al., 1998). Enforcing speed limits requires sufficient funding and time. The issue of speed control is to ensure the safety of the public. Hence, it is one of the top priorities of most jurisdictions. However, the required funding for the process causes restrictions on the methods used to control speed. Some jurisdictions dispatch traffic police that have the authority to stop vehicles who make traffic violations, which includes speeding, to fine the violators. Though this method is somewhat effective, it costs tremendous amounts of money for every hour such law enforcement is dispatched: Also, the effectiveness of this method is restricted to the time that the law enforcing officers were dispatched. They would not be monitoring the same segment of a road all the time. If most drivers drive at great speeds or disrespeCt law enforcement, then dispatching many law enforcing vehicles on a daily basis in an attempt to reduce the speeding of many vehicles would be deemed ineffective. In many cities, where speeding vehicles cause much damage due to colossal accidents, different methods of speed control are being used. Some cities use mobile radar cameras that are randomly placed clandestinely on the side of a road that would take the photograph of the license plates of violating vehicles. Thls method surprises many speeding drivers that may cause many drivers to take caution as they drive on the roads. Some jurisdictions place fixed photo radar cameras on the side of major roads, where speeding vehicles are found in large amounts. This attempts to reduce the overall speed 12

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violations that occur in such major roads during all times. Sometimes, signs are placed to warn people in the existence of such cameras on the road, such that speeding vehicles start reducing their speeds before reaching the photo radar cameras. This is done as not to sw.prise speeding vehicles. Though speeding is dangerous, pushing the brakes at once when a driver discovers a photo radar camera on the side of a freeway may even cause further danger (Portans, 1988). Portans (1988) also concluded that media publicity is also an important factor for reducing speeds if signs are chosen not to be used. 3.2 Necessary Factors for Alternative Speed Enforcement Maintaining photo radar cameras can become very expensive, causing some jurisdictions to use fake cameras or signs to fool drivers. In Boulder, Colorado for example, a programme was initiated for cameras to be used to enforce red lights and speeding limits during green time. In 2000, the programme cost $568,076 to operate, while only $463,803 was collected in fine revenue (City of Boulder Agenda, 2000). Hence, it seems that deploying photo radar cameras are very expensive, and sometimes not as as required. Though this method may allow the reduction of accidents causing injuries, death, and property damage, which will cost a lot more than the deployment of these cameras, jurisdictions are liable to allocate public funding that needs to try to maintain the cost. Hence, if an alternative to photo radar cameras can be found that will be as effective as the cameras but not as costly, then jurisdictions would rather use those alternates. According to the City of Boulder's assessment of the effectiveness of the photo enforcement programs indicate that they are highly effective in reducing speeding in direct proximity to the camera. The limitation is that the speed reduction is only effective for a short distance directly in advance of the device. One of the advantages reported is that it is less costly to operate than mobile photo-radar cameras. According to a British research, photo radar equipment is expensive to buy, operate and maintain; the supporting prosecution procedures also incur substantial administrative 13

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costs. However, the costs are small compared to the benefits to society and the economy (Hooke, Knox and Portas, 1996). Local authorities and the police often have insufficient funds to make fullest use of photo radar cameras to deal with speeding problems. for speed enforcement have to compete with other priorities from the limited budget allocations, while some authorities simply cannot afford it. Some authorities consider using alternative funding strategies to deal with the costs of installing, operating, and maintaining photo radar cameras. There are several types of costs and benefits that are regularly addressed by traffic agencies to analyse the appropriateness of installing and maintaining speed cameras. Besides the obvious costs of the equipment, installation, operation, and maintenance of the speed cameras, there are costs associated to the courts or the agency prosecution service. There are also more costs associated to publicity campaigns. In another note, the cost of planning site locations for the cameras also burden agencies with more expenditure. Benefits are more complex to estimate, since some benefits are intangible. There are savings in human life and injury, as also savings from reduced property damage. There are savings experienced by the police and emergency services as a result of attending to fewer road accidents. There are savings experienced by the health services as a result of fewer accidents. Income from fines generated is probably the simplest tangible benefits type that can be estimated. With less accidents due to speeding, traffic flow will be improved, reducing trip times and improved environment as a result of fewer emissions. This thesis compares the effectiveness of the two other alternatives mentioned, dummy cameras and false signs, with the actual photo radar cameras. For example, on roads that are close to small airports, some jurisdictions may put signs that state the existence of speed radar detection from airplanes. When drivers read the sign and see some small airplanes flying or landing, they may be fooled to believe that some may actually be detecting speeding vehicles, and consequently reduce their speeds. In some cities that use many photo radar cameras, sometimes also use fake cameras in some roads or segments of 14

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a road that has actual cameras. Fake cameras are inserted within a casing to give the appearance that an actual photo radar camera is in place. When drivers pass by those fake \.cameras, some may be fooled and subsequently reduce their speeds. Such methods are more economical than having actual photo radar cameras, since fake cameras or signs do not require much maintenance. Tbis thesis questions whether such methods are effective to decrease the number of speeding violators. 3.3 Factors that May Hinder the Use of Photo Radar Cameras Some companies m countries that use photo radar extensively, such as the United Kingdom, sell or provide information to the public about the locations of fixed photo radar cameras. Some companies provide global coordinates of their locations that some users record it in their global positioning tracking system to be able to circumvent a speeding fine, by reducing speeds when nearing fixed photo radar locations. Tbis information allows people to speed in road segments that a fixed photo radar camera is not installed. Traffic managers, in such cases, try to also use mobile photo radar cameras that may surprise speeding drivers. The information of the locations of mobile photo radar cameras can still be made public when truckers may use the Citizen Band radio to warn other listeners on the whereabouts of the cameras. There are some local radio stations that also accept calls from drivers who give them information on the locations of the mobile cameras, as the locations are then broadcasted over the airwaves to the public. Some traffic managers find that publicising such information can render those cameras to be less effective. However, if publicising the information will allow drivers to reduce their speeds as they near camera locations, would that not mean that the cameras are doing their job? Though the public may know the locations where those cameras may be found, they are not guaranteed that the cameras are not found elsewhere. 15

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Accorcling to the theory that suggests that publiGising the existence of photo radar cameras can allow the reduction of speed by drivers, some authorities place signs that warn drivers in the existence of those cameras. The local governmental entity that manages traffic deliberately publicises this information. Some jurisdictions in the United Kingdom and Australia, for example, record the locations of fixed radar cameras in the public domain. The main reason why photo radar cameras are installed is not to catch drivers unguarded, but instead, to control their speeds. If publicity will cause that, then it may be effective. As stated in the previous section, sometimes signs are placed to falsely claim the existence of the photo radar cameras, only to try to manage drivers' speed. Sometimes when new cameras are installed, their locations are made to the public through the media (i.e. newspapers, television, radio, internet). The Ontario Ministry of Transportation made an eleven-month pilot study in the Province of Ontario beginning on the summer of 1994 by using four portable photo radar units on selected sections of roadways. The study used test sections that were equipped with photo radar cameras and a control section not using the equipment. The roads used were diverse in type. The sites included a 6 lane, 100 km/h divided freeway with urban commuter traffic, a 4 lane, 100 km/h divided highway with recreational traffic, and a 2 lane, 80 km/h undivided highway with urban commuter traffic. The loops embedded in the roadways collected data 24 holl:rs a day all days of the week on vehicle speeds and sizes. Researchers concluded from the data that the proportion of speecling vehicles declined at all sites, inclucling controlled sites, but a greater speed decline was found in sites equipped with photo radar cameras. The reduction of speeds at control sites was attributed to the media coverage of the use of photo radar at some sites, affecting drivers' behaviour. Daily radio announcements reminded drivers of the existence of photo radar cameras on the 6 lane road, which caused it to have the greatest speed reductions. The other sites did not attract much media attention. Hence, researchers have concluded that public media campaigns can lower 16

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average speeds and the proportion of speeders. The study also found that for a short time, the presence of signing that falsely claim the existence of photo radar cameras also reduced drivers' speeds. The study concluded that the existence of photo radar cameras and media publicity would be most effective to reduce drivers' speed. 17

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4. Methodology 4.1 Collecting Data The data collected for research are the speeds of vehicles on roads that have in place one of the three different methods to control speed, fixed radar detecting cameras, fake cameras, and fake signs, that claim the existence of detecting cameras, when truly none are in place. Data for the speeds of vehicles were collected in three different time frames (1) before any of the mentioned methods were installed, (2) within the first two weeks of their use, and (3) after at least one year since they started to be used. All the speed data collected were during levels of service B or better. This is to ensure that vehicles had as close to free-flow conditions as possible to be certain that vehicles had the ability to speed if they choose to, without having any hindrance or obstructions. The speed data were collected in 15 minute time frames during randomly selected times of low levels of service. The 15 minute random time frames were collected daily for about two weeks. The data taken before any speed controllers were placed were collected at most.12 months before any of the speed controllers were placed. Immediately within the first two weeks since the speed controllers were placed, data was collected in a similar manner, during times of low levels of service. More speed data were collected at least after 12 months since the placement of the speed .controllers using the same process. All the data used for this analysis was for the two leftmost lanes, since they are the lanes that are in the range of thepeed cameras. Hence, only the data from the two leftmost lanes of all the roads in this study analysed. All the roads in this with speed limits of less than 100 km/h had only two lanes, while all other roads had 4 lanes, though only the data from the two leftmost lanes were analysed. 18

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Data was collected from different types of roads with different speed limits. Eight of the roads used in the study have radar detector cameras, four of which are in an 80 kph speed limit, one is in 110 kph speed limit, and three in a 120 kph speed limit zones. Four other roads have dummy cameras, one of which is in a 60 kph speed limit, two in an 80 kph speed limit, and another in a 120 kph speed limit zones. Three different roads have signs placed claiming the existence of cameras when none were actually in place. In this category, one road is in an 80 kph, 110 kph, and a 140 kph speed limit zones. 4.2 Data Analyses Statistical analyses were done on each road to identify the significance of the change of driver behaviour trend when fixed photo radar cameras, dummy cameras, and signs falsely claiming the existence of cameras are installed. The speed data was analysed based mainly on the following statistics (1) mean speed, (2) 851h percentile speed, (3) standard deviation, (4) percentage of motorists driving under the speed limit, and (5) percentage of motorists driving under 1 0 km/h over the speed limit. 19

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5. Speed Data Analysis and Results 5.1 Statistical Analysis As discussed in Section 4.2, different techniques of data analyses were made on the collected data. This chapter compares the results of the effectiveness of the different methods of speed control, photo radar cameras, dummy cameras, and false signs. 5.2 Results for Fixed Photo Radar Cameras Results of the data analysed has shown that fixed photo radars are very effective in controlling speed over a highway or street. Table 5.1 shows comparison of the vehicle percentile that did not exceed the speed limit before, immediately, and after the installation of fixed photo radar cameras. It can be observed from the data that this method is very effective in reducing the numbers of speeding vehicles. 20

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Table 5.1 Effectiveness of Fixed Photo Radar Cameras in Controlling Speed of Vehicles that Do Not Exceed the Speed Limit Percentile Speed Limit Before Immediately After kph Installation Installation Installation Road 2 80 30.22 53.29 68.81 Road 7 80 54.79 79.89 88.64 Road9 120 55.20 80.84 90.21 Road 11 120 49.48 69.26 90.63 Road 12 120 47.66 50.48 86.40 Road 13 110 32.82 58.72 92.24 Road 14 80 47.52 72.18 77.23 Road 15 80 29.89 50.54 76.76 AVERAGE 43.45 64.40 83.87 In Table 5.2, the same data is compared for vehicles that did not exceed 10 km/h over the speed limit. We can observe that on average about 98% of vehicles do not exceed 10 kph after the installation of the fixed photo radar cameras. This is dramatically significant in reducing the number of speeding vehicles. Before the installation of the photo radar cameras, about only an average of 7 5% of vehicles travelled below .1 0 km/h over the speed limit. 21

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Table 5.2 Effectiveness of Fixed Photo Radar Cameras in Controlling Speed of Vehicles that Do Not Exceed 10 km/h over the Speed Limit Percentile 10 kph Over Before Immediately After Speed Limit Installation Installation Installation RQad2 90 82.78 89.67 98.49 Road 7 90 99.82 99.54 99.73 Road9 130 71.04 94.39 97.11 Road 11 130 68.78 85.07 99.03 Road 12 130 62.95 68.02 93.29 Road 13 120 44.82 84.83 98.10 Road 14 90 89.90 96.27 98.35 Road 15 90 76.79 91.24 99.62 AVERAGE 74.61 88.63 97.97 When comparing results from other studies in regards to the speed reduction attributed to speed cameras, it has shown that on average, the 85th percentile speeds are dropped. by about 10%. Results from this. study show a similar pattern. It is also apparent that the higher the speed limit, the more percent change of speed is noticed. Seemingly, speed limits of over 100 km/h show reduced speeds of more than 10%, while speed limits less than 100 km/h show reduced speeds by less than 10%. Tbis does not necessarily mean that fixed radar cameras are more effective with higher speed limits. The speed limits that are over 100 km/h in this study are freeways. Since the data taken for this study was made during levels of service of B or better, many drivers on freeways speed relatively much higher than drivers on other types of roads, since on other roads there are factors that may help the reduction of speed, such as traffic lights or other physical restrictions. Table 5.3 records the 85th percentile speeds of vehicles that were monitored by fixed photo radar cameras that are used in this study. For comparison reasons, Table 5.4 shows 85th percentile speeds of vehicles that were recorded in Australia. It is apparent that fixed photo radar installations are more effective in roads with greater speed limits. Apparendy, the 22

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higher the speed limit, the more dramatic the change in the speeds of vehicles is observed, when the road is controlled by the fixed radar cameras. Table 5.3 85th Percentile Speeds on Roads Monitored by Fixed Photo Radar Cameras 85th Percentile Speed Limit Before Immediately After kph kph kph kph Road 2 80 91 87 84 Road7 80 85 81 78 Road 9 120 139 121 118 Road 11 120 149 129 119 Road 12 120 148 138 119 Road 13 110 139 120 107 Road 14 80 88 83 82 Road 15 80 93 87 84 23

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I Table 5.4 85th Percentile Speeds on Roads Monitored by Fixed Photo Radar Cameras in Australia Woy Woy Rd, Kariong (Speed 88 km/h 78 km/h limit 80 km/h) Pacific Hwy, Herons Creek 110 km/h 101 km/h -102 km/h {Speed limit 100 km/h) New England Hwy, Tilbuster 110 km/h 99 km/h -1 02 km/h {Speed limit 100. km/h) Hume Hwy, Tarcutta{Speed 111 km/h 106 km/h limit 100 km/h) Cowpasture Road, Green Valley-80 km/h 67 km/h (Speed limit 70 km/h) Delhi Road, Macquarie Park -71 km/h 66 km/h (Speed limit 60 km/h) New England Hwy, Lochinvar -78 km/h 64 km/h (Speed limit 60 km/h) Bells Line of Road, Kurrajong 79 km/h 64 km/h Heights -(Speed limit 60 km/h) Princes Hwy, Nth. Wollongong70 km/h 62 km/h (Speed limit 60 km/h) Newcastle Road, Lambton -78 km/h 72 km/h (Speed limit 70 km/h) I 5.3 Results for Dummy Photo Radar Cameras Dummy cameras have been shown to be not as effective as actual fixed photo radar cameras. Though dummy cameras reduce the speed of vehicles in a statistical significance, this change is. not drama.tic. Table 5.5 shows a comparison of the vehicle percentile that did not exceed the speed limit before, immediately, and after the installation of fixed photo radar cameras. When we make a ratio comparison on the averages of how effective fixed radar cameras are in reducing speeds and how dummy cameras are, then immediately after the installation of both fixed and dummy cameras it can be shown that they both have the 24

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same affect after installation. On average, 68% of vehicles reduce the speeds immediately after the installation when compared to before the installation of both fixed and dummy cameras. However, in the long-term, the dummy cameras decrease their effectiveness in controlling speeding vehicles, though they reduce the speed of vehicles when compared to before the installation. Table 5.5 Effectiveness of Dwnmy Photo Radar Cameras in Controlling Speed ofVehicles that Do Not Exceed the Speed Limit Percentile Speed Limit Before Immediately After kph Installation Installation Installation Road 1 80 40.25 73.17 59.68 Road 6 60 45.80 72.50 46.89 Road 8 80 67.40 84.89 70.90 Road 10 120 50.17 63.19 52.36 AVERAGE 50.91 73.44 57.46 In Table 5.6, the same data is compared for vehicles that did not exceed 10 km/h over the speed limit. We can observe that on average about 95% of vehicles did not exceed 10 kph immediately after the installation of the dummy photo radar cameras. However, in the long-term, more vehicles start to speed again having on average of only about 87% of vehicles not exceeding 1 0 kph over the speed limit. Though this still reduces the number of speeding vehicles than before the installation of the dummy cameras, they are not nearly as effective as the fixed .photo radar cameras. It is possible that, in the long-term, drivers who frequently use the road realise that the dummy cameras do not take photos of violating vehicles. Drivers possibly know it by recognising that the flash does not go on during the night when speeding drivers know they are violating the law, or when drivers know they have violated the speeding law when they passed the dummy camera and realise that they have not been fined. The main reason why more vehicles still reduce their speeds 25

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is possibly caused by infrequent travellers who may have not, yet, realised that a dummy camera is actually installed. Table 5.6 Effectiveness of Dummy Photo Radar Cameras in Controlling Speed ofVehicles that Do Not Exceed 10 km/h over the Speed Limit Percentile 10 kph Over Before Immediately After Speed Limit Installation Installation Installation Road 1 90 82.22 94.56 92.18 Road6 70 74.10 91.27 83.33 Road 8 90 98.79 99.44 99.63 Road 10 130" 66.32 93.98 74.35 AVERAGE 80.36 94.81 87.37 Table 5.7 records the 851h percentile speeds of vehicles that were monitored by dummy photo radar camera installations that are used in this study. This table also shows further that although immediately after the installation of the dummy cameras, many drivers reduce their speeds. This speed is later resumed when evidently frequent drivers realise that no camera is actually taking photos of violating vehicles. Hence, knowing that fixed photo radar cameras are expensive to install and maintain, then dummy cameras can only be effective on roads that are not used by frequent drivers. Thus, it may be used in lllghways that are travelled by infrequent drivers, such as in rurallllghways between cities. Only then could it be possibly more effective than being used in urban or suburban roads as an alternative to fixed photo radar cameras. 26

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Table 5.7 85th Percentile Speeds on Roads with Dummy Photo Radar Cameras Installed 85th Percentile Speed Limit Before Immediately kph kph kph Road 1 80 90 84 Road 6 60 73 67 Road 8 80 84 80 Road 10 120 145 126 5.4 Results for Signs Claiming the Existence of Fixed Photo Radar Cameras After kph 87 70 84 141 Signs that falsely claim the existence of fixed photo radar cameras seem to be as effective as dummy cameras. When doing comparisons from Table 5.8, the before and after data fall in the same trend as dummy cameras. 12% more vehicles reduce their speeds in a long term after the installation in both dummy cameras and signs. Thus, we may also conclude that frequent drivers realise that signs falsely claim the existence of the fixed cameras. Frequent drivers probably try to identify the location of the fixed photo tadar cameras, but recognise their inexistence after a while. Nonetheless, fixed photo radar cameras, dummy cameras, and signs seem to have the same effects immediately after their installations. On average, each of them reduces the number of speeding vehicles by an extra 32% below the speed limit. 27

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Table 5.8 Effectiveness of Signs Falsely Claiming the Existence of Fixed Photo Radar Cameras in Controlling Speed of Vehicles that Do Not Exceed the Speed Limit Percentile Speed Limit Before Immediately After kph Installation Installation Installation Road 3 80 49.57 70.48 63.68 Road4 140 73.35 85.80 78.62 RoadS 110 64.13 72.00 70.64 AVERAGE 62.35 76.09 70.98 In Table 5.9, the data is compared for vehicles that did not exceed 10 km/h over the speed limit. We can observe that on average about 93% of vehicles did not exceed 10 kph immediately after the installation of the signs. However, in the long-term, more vehicles started to resume their speeds again having on average of only about 91% of vehicles not exceeding 10 kph over the speed limit. Though this still reduces the number of speeding vehicles than before the installation of the signs, they are similar to the dummy cameras by not being nearly as effective as the fixed photo radar cameras. Like dummy cameras, it is possible that, in the long-term, drivers who frequendy use the road realise that the signs falsely claim the existence of the fixed radar cameras. The data for this thesis is in parallel with the conclusions made in a study by the Ontario Ministry of Transportation in 1994, where the study has also found that signs, which falsely claim the existence photo radar cameras are only effective for a very short time. The main reason why more vehicles still reduce their speeds in the long term, when compared to before the installation of the signs, is possibly caused by infrequent travellers who may have not, yet, realised that the signs falsely claim the existence of the fixed photo radar cameras. 28

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Table 5.9 Effectiveness of Signs Falsely Claiming the Existence of Fixed Photo Radar Cameras in Controlling Speed of Vehicles that Do Not Exceed 10 km/h over the Speed Limit Percentile 10 kph Over Before Immediately After Speed Limit Installation Installation Installation Road3 90 91.21 96.26 92.56 Road 4 150 86.97 96.70 94.03 Road 5 120 85.47 85.04 86.22 AVERAGE 87.88 92.67 90.94 Table 5.10 records the 851h percentile speeds of vehicles that were monitored by dummy photo radar camera installations that are used in this study. This table also shows further that although immediately after the installation of the dummy cameras, many drivers reduce their speeds. This speed is later resumed when evidently frequent drivers realise that no camera is actually taking photos of violating vehicles. Hence, knowing that fixed photo radar cameras are expensive to install and maintain, then dummy cameras can only be effective on roads that are not used by frequent drivers. Thus, it may be used in highways that are travelled by infrequent drivers, such as in rural highways between cities. Only then could it be possibly more effective than being used in urban or suburban roads as an alternative to fixed photo radar cameras. 29

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Table 5.10 85th Percentile Speeds on Roads with Signs Falsely Claiming the Existence of Fixed Photo Radar Cameras Installed 85th Percentile Speed Limit Before Immediately After kph kph kph kph Road3 80 86 86 86 Road4 140 148 139 143 RoadS 110 119 119 118 5.5 Conclusion and Recommendations After analysing the three methods of speed control in this thesis (1) fixed photo enforcement, (2) dummy photo cameras, and (3) signs falsely claiming the existence of photo enforcement when they actually do not exist, it can be observed from the data collected that fixed photo radar enforcement is the most effective mean to reduce speed. Tbis analysis agrees with other studies in literature that found great success in the usage of photo radar cameras. Tbis study has also demonstrated that dummy photo radar cameras are only effective in the short-term, though the average speeds in the long-term are still less than the average speeds before their installation. Tbis phenomenon is presumed to be caused by frequent users of the road, who in the long-term, apprehend that a dummy photo radar camera is placed, resulting in their resumption of prior speeds. However, it is also supposed that infrequent travellers on the road might still imagine that an actual photo radar camera is installed, and therefore reduce their speeds. Tbis might be the basis on why dummy photo radar cameras would still reduce average speeds in the long-term, but not as effective as actual cameras. Results for the usage of signs that falsely claim the existence of photo radar cameras are similar to that of the usage of dummy cameras. Though in the short-term they may be 30

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effective, they lose their efficacy m the long-term. This is also possibly due to the recognition of frequent travellers of the false claim that the signs are making, while infrequent travellers may still reduce their speeds, causing the average speeds to be still slighdy less than it was before the placement of the signs. A study in Australia has also made a similar conclusion in a 1994 study (Ministry of Transportation of Ontario, 1997). Due to the results and conclusions made in this thesis, it may be recommended to place dummy cameras or false signs on rural roads or highways that may not be used by frequent travellers. Most urban roads and highways are used by frequent travellers; thus, reducing the usefulness of dummy cameras and false signs. In. such cases, actual speed photo radar enforcement may be necessary. Future studies are recommended to research how combinations of different methods of traffic speed control can affect drivers' behaviour. Publicising the locations of speed cameras, by using true signs or the media, may allow more efficacy as a study in Australia has suggested. However, more research in that matter needs to be made to make a better conclusion. 31

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APPENDIX A. Summary Charts and Tables ofTraffic Speed Data of Monitored Roads Chart A.l Road 1 Histogram Before Data Road 1 Before Data 300 -.-----------. 120.00% 250 >. 200 (,) s:::: 150 C"' u.. 100 ...-CQCO(")Of'-I.ONO'> "<:t"<:ti.OCOf'-f'-COO'>O'> Speed (kph) 100.00% 80.00% 60.00% 40.00% 20.00% .00% 32 l.:;tc:o'''' Frequency -aCumulative %

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Chart A.2 Road 1 Histogram Immediately After Data Road 1 -Histogram Short-Term Data 200 .-------------. 120.00% 180 160 >o 140 g 120 100 C"' 80 LL 60 40 20 0 Speed (kph) 100.00% 80.00% 60.00% 40.00% 20.00% .00% 33 Frequency --a---Cumulative %

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Chart A.3 Road 1 Histogram After Data CJ t: G) :l C" e LL Road 1 -Histogram Long-Term Data 300 250 200 150 100 50 OJ'..-.;::t"OJ'..-.;::t...-COLC> -.;::t-.;::t"LC>COCOI'--COCOO) Speed (kph) 120.00% 100.00% 80.00% Frequency 60.00% ---e-Cumulative % 40.00% 20.00% .00% 34

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Table 4-1 Road 1 Summary Before Immediately After Mean 80.226 Mean 72.205 Mean 75.063 Standard Standard Standard Error 0.209 Error 0.249 Error 0.229 Median 82 Median 74 Median 77 Mode 89 Mode 80 Mode 79 Standard Standard Standard Deviation 10.683 Deviation 12.731 Deviation 11.703 Sample Sample Sample Variance 114.133 Variance 162.091 Variance 136.966 Kurtosis 0.647 Kurtosis -0.296 Kurtosis -0.066 Skewness -0.710 Skewness -0.549 Skewness -0.617 Range 101.398 Range 86.315 Range 86.356 Minimum 10.602 Minimum 12.685 Minimum 11.644 Maximum 112 Maximum 99 Maximum 98 Sum 209630 Sum 188671 Sum 196141 Count 2613 C_ount 2613 Count 2613 Confidence Confidence Confidence Level(95.0%) 0.410 Level(95.0%) 0.488 Level(95.0%) 0.449 35

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Chart A.4 Road 2 Histogram Before Data >. 150 (.) c C1) ::::J C"' u.. 100 50 Road 2 -Histogram Before Data Speed (kph) 120.00% 100.00% 80.00% 60.00% 40.00% 20.00% .00% 36 1':'''-'0''.1 Frequency --!:JCumulative%

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Chart A.5 Road 2 Histogram Immediately After Data Road 2 -Histogram Short-Term Data 200 --.------------.120.00% 150 c: Q) :::l C" Q) ... LL 100 50 Speed (kph) 100.00% 80.00% 60.00% 40.00% 20.00% .00% 37 l':o'';"'"A Frequency ----s-Cumulative %

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Chart A.6 Road 2 Histogram After Data Road 2-Histogram Long-Term Data 200 ...-----------. 120.00% >o 150 CJ s:::::: 100 C" C1) ... u.. 50 bt" t:O"-t:OOJ Speed (kph) 100.00% 80.00% 60.00% 40.00% 20.00% .00% 38 b' Frequency ----mCumulative %

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Table A.2 Road 2 Summary Before Immediately After Mean 83.104 Mean 78.686 Mean 74.204 Standard Standard Standard Error 0.169 Error 0.185 Error 0.210 Median 84 Median 80 Median 76 Mode 80 Mode 82 Mode 79 Standard Standard Standard Deviation 8.608 Deviation 9.417 Deviation 10.655 Sample Sample Sample Variance 74.091 Variance 88.685 Variance 113.538 Kurtosis 0.504 Kurtosis 0.250 Kurtosis -0.118 Skewness -0.579 Skewness -0.606 Skewness -0.621 Range 60 Range 59 Range 55 Minimum 40 Minimum 40 Minimum 41 Maximum 100 Maximum 99 Maximum 96 Sum 214741 Sum 203325 Sum 191743 Count 2584 Count 2584 Count 2584 Confidence Confidence Confidence Level(95.0%) 0.332 Level(95.0%) 0.363 Level(95.0%) 0.411 39

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Chart A. 7 Road 3 Histogram Before Data Road 3 Histogram Before Data 160 -.-----------.-120.00% 140 120 100 s::::: 80 C"' 60 LL 40 20 0 T"""f'-('I)O')I.()T"""f'-('1)0')1.() I.()I.()C0C01'-COC00')0')0 Speed (kph) 80.00% 60.00% 40.00% 20.00% 40 ,_,._-;,;,! Frequency --!'!!--Cumulative%

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Chart A.S Road 3 Histogram Immediately After Data Road 3-Histogram Short-Term Data 180 -.---------------r-120.00% 160 140 120 s::::: Cl) 100 g. 80 60 LL OCONCOVOCONCO LOLOCOCOI'-COCOO')O') Speed (kph) 100.00% 80.00% 60.00% 40.00% 20.00% 41 -Cumulative %

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Chart A. 9 Road 3 Histogram After Data Road 3-Histogram Long-Term Data 200 >. g 150 Q) ::I C" 100 LL 50 Speed (kph) 80.00% 60.00% 40.00% 20.00% .00% 42 lm-'4:':1 Frequency -aCumulative %

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Table A.3 Road 3 Summary Before Immediately After Mean 80.474 Mean 76.035 Mean 76.439 Standard Standard Standard Error 0.156 Error 0.210 Error 0.208 Median 81 Median 77 Median 77 Mode 85 Mode 80 Mode 77 Standard Standard Standard Deviation 7.366 Deviation 9.881 Deviation 9.794 Sample Sample Sample Variance 54.261 Variance 97.640 Variance 95.929 Kurtosis 0.056 Kurtosis -0.234 Kurtosis -0.752 Skewness 0.040 Skewness -0.450 Skewness -0.052 Range 56 Range 51 Range 53 Minimum 51 Minimum 50 Minimum 50 Maximum 107 Maximum 101 Maximum 103 Sum 178571 Sum 168721 Sum 169618 Count 2219 Count 2219 Count 2219 Confidence Confidence Confidence Level(95.0%) 0.307 Level(95.0%) 0.411 Level(95.0%) 0.408 43

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Chart A.lO Road 4 Histogram Before Data Road 4 Histogram Before Data 600 -.------------,--120.00% 500 400 1: g 300 C"' f u.. 200 LDI'-0>T"""ML01'-0>T"""C') I'-C00>T"""C\IM"'LOI'-CO Speed 80.00% 60.00% 40.00% 20.00% 44 1-:>.i.,l Frequency -a--Cumulative %

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Chart A.11 Road 4 Histogram Immediately After Data Road 4-Histogram Short-Term Data 500 120.00% 450 400 100.00% 350 80.00% g 300 250 60.00% [ 200 lL. 150 40.00% 100 20.00% 50 I'C:OO>ONM"<::t"COI'C:O Speed (kph) 45 '"'!';'-"''Frequency -GI-Cumulative %

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Chart A.12 Road 4 Histogram After Data Road 4-Histogram Long-Term Data 600 500 400 (,) c:: G) 300 ::I C" e 200 LL 100 COC.OON-.;:tCOC.OON" I'-C.00T"""NC'?"COI'-C.O Speed {kph) 120.00% 100.00% 80.00% 60.00% '"'J>S'I Frequency -Cumulative % 40.00% 20.00% 46

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Table A.4 Road 4 Summary Before Immediately After Mean 130.903 Mean 127.288 Mean 128.307 Standard Standard Standard Error 0.152 Error 0.135 Error 0.143 Median 131 Median 129 Median 129 Mode 114 Mode 120 Mode 113 Standard Standard Standard Deviation 15.644 Deviation 13.883 Deviation 14.696 Sample Sample Sample Variance 244.730 Variance 192.746 Variance 215.960 Kurtosis -0.198 Kurtosis 0.087 Kurtosis -0.077 Skewness -0.108 Skewness -0.274 Skewness -0.194 Range 116 Range 118 Ran_g_e 112 Minimum 75 Minimum 70 Minimum 76 Maximum 191 Maximum 188 Maximum 188 Sum 1379067 Sum 1340984 Sum 1351719 Count 10535 Count 10535 Count 10535 Confidence Confidence Confidence Level(95.0%) 0.299 Level(95.0%) 0.265 Level(95.0%) 0.281 47

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Chart A.13 Road 5 Histogram Before Data Road 5 Histogram Before Data 500.------------. 120.00% 450 400 100.00% 350 g 300 250 60.00% e-200 150 100 20.00% 50 0 0') N I!) CO ..--.:t ,..._ 0 C"') C.O ,..._0')0..-C"')-.;f"L!),....C()O') Speed (kph) 48 Frequency --Cumulative %

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Chart A.14 Road 5 Histogram Immediately After Data Road 5-Histogram Short-Term Data 600 -,--------------r 120.00% 500 100.00% 400 80.00% c 300 60.00% C" 200 40.00% 100 0 20.00% Rlll!nlftl!m!!mi!IIM!Iilllll. 00% Speed (kph) 49 Frequency -aCumulative %

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Chart A.15 Road 5 Histogram After Data Road 5-Histogram Long-Term Data 700 120.00% 600 100.00% 500 80.00% >CJ c 400 1,1'''<"' Frequency (1) 60.00% ::::J C" 300 ----,2Cumulative % (1) ... 40.00% u.. 200 100 20.00% 0 COCJ)O..-N('t)'o:::tlO
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Table A.5 Road 5 Summary Before Immediately After Mean. 107.680 Mean 105.806 Mean 106.483 Standard Standard Standard Error 0.126 Error 0.132 Error 0.118 Median 106 Median 103 Median 104 Mode 92 Mode 101 Mode 96 Standard Standard Standard Deviation 12.047 Deviation 12.638 Deviation 11.263 Sample Sample Sample Variance 145.135 Variance 159.710 Variance 126.854 Kurtosis 2.450 Kurtosis 0.723 Kurtosis 0.303 Skewness 0.962 Skewness 0.870 Skewness 0.907 Range 121 Range 126 Range 93 Minimum 79 Minimum 72 Minimum 81 Maximum 200 Maximum 198 Maximum 174 Sum 983336 Sum 966224 Sum 972400 Count 9132 Count 9132 Count 9132 Confidence Confidence Confidence Level(95.0%) 0.247 Level(95.0%} 0.259 Level(95.0%} 0.231 51

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Chart A16 Road 6 Histogram Before Data Road 6 Histogram Before Data 160 ......------------,-120.00% 140 120 100 c 80 C"' e 6o I.L Speed (kph) 80.00% 60.00% 40.00% 20.00% 52 -19-Cumulative %

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Chart A.17 Road 6 Histogram Immediately After Data c:: Q) ::J C" I!! LL. Road 6 Histogram Short-Term Data 250 200 150 100 50 C'\IC:O-..t"OC.OC'\IC:O-..t"OC.O(].) MM"'::t"LOLOC.OC.OI'--C:OC:0(5 2 Speed (kph) 120.00% 100.00% 80.00% 60.00% !:.:.:,.-! Frequency -cumulative% 40.00% 20.00% 53

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Chart A.18 Road 6 Histogram After Data Road 6 Histogram Long-Term Data 140 -.------------,--120.00% 120 100 c 80 Q) [ 60 LL 40 20 80.00% 60.00% 40.00% 20.00% 0 .00% C'?C'?"<:t"<:ti.OCOCOI'-1'-CO Speed 54 ,,.,r'"'' Frequency --m-Cumulative %

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Table A.6 Road 6 Summary Before Immediately After Mean 61.826 Mean 56.911 Mean 61.183 Standard Standard Standard Error 0.215 Error 0.191 Error 0.196 Median 62 Median 56 Median 61 Mode 47 Mode 54 Mode 65 Standard Standard Standard Deviation 10.478 Deviation 9.320 Deviation 9.542 Sample Sample Sample Variance 109.779 Variance 86.856 Variance 91.052 Kurtosis -1.118 Kurtosis 0.512 Kurtosis -0.550 Skewness 0.064 Skewness 0.679 Skewness -0.041 Range 58 Range 59 Range 58 Minimum 31 Minimum 32 Minimum 31 Maximum 89 Maximum 91 Maximum 89 Sum 147269 Sum 135561 Sum 145739 Count 2382 Count 2382 Count 2382 Confidence Confidence Confidence LeveL(95.0%) 0.421 Level(95.0%) 0.374 Level(95.0%) 0.383 55

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Chart A.19 Road 7 Histogram Before Data 200 g 150 Cl) :::1 C" e 1oo u. 50 Road 7-Histogram Before Data 80.00% 60.00% 40.00% 20.00% Speed (kph) 56 ,.,,:'! Frequency -a-Cumulative%

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Chart A.20 Road 7 Histogram Immediately After Data Road 7-Histogram Short-Term Data 180 120.00% 160 100.00% 140 120 100 5-80 e u.. 60 40 20 0 OCO Speed (kph) 80.00% 60.00% 40.00% 20.00% .00% 57 Frequency ---QCumulative %

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Chart A.21 Road 7 Histogram After Data Road 7 Histogram Long-Term Data 200 -.--------------.-120.00% 180 160 140 >. C.) c: G) j tr f LL 120 100 80 60 Speed (kph) 80.00% 60.00% 40.00% 20.00% 58 -Cumulative %

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Table A.7 Road 7 Summary Before Immediately After Mean 79.301 Mean 73.795 Mean 73.097 Standard Standard Standard Error 0.132 Error 0.164 Error 0.152 Median 80 Median 74. Median 74 Mode 82 Mode 72 Mode 77 Standard Standard Standard Deviation 6.174 Deviation 7.654 Deviation 7.111 Sample Sample Sample Variance 38.113 Variance 58.584 Variance 50.572 Kurtosis 4.247 Kurtosis 0.445 Kurtosis 0.299 Skewness -1.137 Skewness -0.145 Skewness -0.203 Range 58 Range 58 Range 58 Minimum 40 Minimum 40 Minimum 41 Maximum 98 Maximum 98 Maximum 99 Sum 173113 Sum 161095 Sum 159571 Count 2183 Count 2183 Count 2183 Confidence Confidence Confidence Level(95.0%) 0.259 Levei(9S.O%) 0.321 Level(95.0%) 0.298 59

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Chart A.22 Road 8 Histogram Before Data 180 160 140 >. u 120 c 100 Cl) ::I C" 80 e u. 60 40 20 0 Road 8-Histogram Before Data 80.00% 60.00% 40.00% 20.00% Speed (kph) 60 k .. ,,_,, Frequency -----&--Cumulative %

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Chart A.23 Road 8 Histogram Immediately After Data Road 8-Histogram Short-Term Data 250 120.00% 200 100.00% 80.00% s:::: 150 Frequency G) 60.00% :::::J C" 100 Cumulative % G) 40.00% LL 50 20.00% Speed (kph) 61

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Chart A.24 Road 8 Histogram After Data Road 8 Histogram Long-Term Data 200 g 150 G) :::::s tT e 1oo LL 50 OCONCO-.::t"OCONCO-.::t -.::t-.::tl.OLOCOI"--1"--COCOO'> Speed (kph) .80.00% 60.00% 40.00% 20.00% .00% 62 '"'''il''' Frequency -----Cumulative %

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Table A.8 Road 8 Summary Before Immediately After Mean 74.823 Mean 74.252 Mean 75.111 Standard Standard Standard Error 0.210 Error 0.155 Error 0.201 Median 75 Median 75 Median 75 Mode 74 Mode 71 Mode 75 Standard Standard Standard Deviation 9.704 Deviation 7.161 Deviation 9.315 Sample Sample Sample Variance 94.173 Variance 51.287 Variance 86.763 Kurtosis 0.180 Kurtosis 1.621 Kurtosis 1.621 Skewness -0.398 Skewness -0.383 Skewness -0.969 Range 65 Range 69 Range 58 Minimum 40 Minimum 39 Minimum 40 Maximum 105 Maximum 108 Maximum 98 Sum 160420 Sum 159196 Sum 161039 Count 2144 Count 2144 Count 2144 Confidence Confidence Confidence Level(95.0%) 0.411 Level(95.0%) 0.303 Leve1(95.0%) 0.395 63

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Chart A.25 Road 9 Histogram Before Data Road 9 Histogram Before Data 400 120.00% 350 300 250 c:: 200 C"' 150 LL 100 50 0 ..-..-..-..-..-NNN Speed (kph) 80.00% 60.00% 40.00% 20.00% 64 !:'.ob::.l Frequency -cumulative%

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Chart A.26 Road 9 Histogram Immediately After Data Road 9 Histogram Short-Term Data 600 500 >-400 u c:::: Cl) 300 ::I C" Cl) .... LL 200 100 0 Q')('t)I'-..-LOQ)('t)I'-..-LO <.OCOQ')..-C\I('t)Ll)<.OCOQ') Speed (kph) 120.00% 100.00% 80.00% 60.00% "''"""''Frequency Cumulative % 40.00% 20.00% 65

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Chart A.27 Road 9 Histogram After Data Road 9-Histogram Long-Term Data 600 120.00% 500 100.00% 400 80.00% c: li".r<'e:l Frequency G,) 300 60.00% j C" ---&-Cumulative % e 200 40.00% u. 100 20.00% 0 Speed (kph) 66

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Table A.9 Road 9 Summary Before Immediately After Mean 119.152 Mean 110.194 Mean 106.818 Standard Standard Standard Error 0.208 Error 0.144 Error 0.132 Median 117 Median 112 Median 108 Mode 117 Mode 118 Mode 117 Standard Standard Standard Deviation 20.321 Deviation 14.046 Deviation 12.920 Sample Sample Sample Variance 412.931 Variance 197.291 Variance 166.933 Kurtosis -0.325 Kurtosis 0.106 Kurtosis -0.083 Skewness 0.216 Skewness -0.133 Skewness -0.111 Range 181 Range 132 Range 99 Minimum 70 Minimum 69 Minimum 70 Maximum 251 Maximum 201 Maximum .. 169 Sum 1137906 Sum 1052350 Sum 1020108 Count 9550 Count 9550 Count 9550 Confidence Confidence Confidence Level(95.0%) 0.408 Level(95.0%) 0.282 Level(95.0%) 0.259 67

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Chart A.28 Road 10 Histogram Before Data Road 10 -Histogram Before Data 400 --.------------.-120.00% 350 300 250 c 200 C"' 150 u.. Speed (kph) 80.00% 60.00% 40.00% 20.00% 68 1\'-'?l''''' Frequency -Cumulative %

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Chart A.29 Road 10 Histogram Immediately After Data Road 10-Histogram Short-Term Data 100 120.00% 600 100.00% 500 80.00% u r::::: 400 f',;erl Frequency (I) :::s 60.00% C'" 300 -aCumulative% LL 40.p0% 200 100 20.00% Speed (kph) 69

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Chart A.30 Road 10 Histogram After Data Road 10 Histogram Long-Term Data 800 -.---------------,--120.00% 700 600 500 : 400 C" 300 LL 72 80.00% 60.00% 40.00% 20.00% 70 1. Cumulative %

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Table A.lO Road 10 Summary Before Immediately After Mean 122.829 Mean 116.223 Mean 122.221 Standard Standard Standard Error 0.189 Error 0.106 Error 0.166 Median 120 Median 118 Median 120 Mode 117 Mode 118 Mode. 121 Standard Standard Standard Deviation 18.758 Deviation 10.524 Deviation 16.466 Sample Sample Sample Variance 351.852 Variance 110.748 Variance 271.142 Kurtosis -0.669 Kurtosis 1.137 Kurtosis 0.116 Skewness 0.338 Skewness -0.316 Skewness 0.520 Range 120 Range 112 Range 117 Minimum 71 Minimum 71 Minimum 73 Maximum 191 Maximum 183 Maximum 190 Sum 1205072 Sum 1140259 Sum 1199112 Count 9811 Count 9811 Count 9811 Confidence Confidence Confidence Level(95.0%) 0.371 Level(95.0%) 0.208 Level(95.0%) 0.326 71

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Chart A.31 Road 11 Histogram Before Data Road 11 Histogram Before Data 450 -,-----------r 120.00% 400 350 .300 u ; 250 :::s C" 200 e I.L 150 100 50 0 Speed (kph) 80.00% 60.00% 40.00% 20.00% .00% 72 1 Frequency %

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Chart A.32 Road 11 Histogram Immediately After Data Road 11 -Histogram Short-Term Data 800 --.---------------. 120.00% 700 600 500 s:::: 400 C" e 3oo u.. 200 100 0 80.00% 60.00% 40.00% 20.00% Speed (kph) 73 ""''"'d Frequency -I!}Cumulative %

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Chart A.33 Road 11 Histogram After Data Road 11 Histogram LongTerm Data 1200 120.00% 1000 100.00% >a 800 80.00% CJ r:::::: '*'"''"' Frequency Q) 600 60.00% ::J C" -a-Cumulative % u. 400 40.00% 200 20.00% ...-......-...-.........-Speed (kph) 74

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Table A.ll Road 11 Summary Before Immediately After Mean 124.060 Mean 119.146 Mean 113.213 Standard Standard Standard Error 0.212 Error 0.151 Error 0.100 Median 121 Median 118 Median 116 Mode 120 Mode 117 Mode 117 Standard Standard Standard Deviation 21.023 Deviation 14.995 Deviation 9.934 Sample Sample Sample Variance 441.983 Variance 224.842 Variance 98.693 Kurtosis -0.203 Kurtosis 1.831 Kurtosis 5.868 Skewness 0.345 Skewness 0.683 Skewness -0.443 Range 124 Range 120 Range 130 Minimum 68 Minimum 70 Minimum 71 Maximum 192 Maximum 190 Maximum 201 Sum 1216041 Sum 1167871 Sum 1109710 Count 9802. Count 9802 Count 9802 Confidence Confidence Confidence Level(95.0%) 0.416 Level(95.0%) 0.297 Level_(95.0% )_ 0.197 75

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Chart A.34 Road 12 Histogram Before Data Road 12 Histogram Before Data 350 -.---------------. 120.00% >u 300 250 ; 200 ::::J 150 LL 100 50 0 80.00% 60.00% 40.00% 20.00% Speed (kph) 76 IEr"''';l Frequency Cumulative %

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Chart A.35 Road 12 Histogram Immediately After Data Road 12 Histogram ShortTerm Data 450 .-----------------r-120.00% 400 350 >. 300 u ; 250 ;::, C" 200 e LL 150 100 50 0 Noo;t
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Chart A.36 Road 12 Histogram After Data Road 12-Histogram Long-Term Data 700 -,-------------.-120.00% 600 100.00% 500 80.00% ; 400 ::J 60.00% g 300 U: 200 40.00% 100 20.00% 0 .00% Speed (kph) 78 Frequency --Cumulative %

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Table A.12 Road 12 Summary Before Immediately After Mean 125.866 Mean 120.766 Mean 109.294 Standard Standard Standard Error 0.211 Error 0.176 Error .157 Median 123 Median 120 Median 109 Mode 125 Mode 137 Mode 112 Standard Standard Standard Deviation 21.708 Deviation 18.142 Deviation 16.138 Sample Sample Sample Variance 471.245 Variance 329.144 Variance 260.433 Kurtosis -0.823 Kurtosis -0.211 Kurtosis 2.179 Skewness 0.254 Skewness 0.087 Skewness 0.999 Range 127 Range 111 Range 135 Minimum 71 Minimum 72 Minimum 59 Maximum 198 Maximum 183 Maximum 194 Sum 1331409 Sum 1277458 Sum 1156116 Count 10578 Count 10578 Count 10578 Confidence Confidence Confidence Level(95.0%) 0.414 Level(95.0%) 0.346 Level(95.0%) 0.308 79

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Chart A.37 Road 13 Histogram Before Data 500 400 c: 300 C" e LL 200 100 0 Road 13 Histogram Before Data C\ILOCO..--.;ti'-OMCOO'> 1'-COO'>..-C\IMLOCOI'-CO Speed (kph) 80.00% 60.00% 40.00% 20.00% 80 Frequency -GCumulative %

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Chart A38 Road 13 Histogram Immediately After Data Road 13-Histogram Short-Term Data BOO ...,------------'------r 120.00% 700 600 500 r:::: 400 tT 300 u.. Speed (kph) 80.00% 60.00% 40.00% 20.00% 81 Cumulative %

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Chart A.39 Road 13 Histogram After Data Road 13-Histogram Term Data 800 .------------,-120.00% 700 600 g 500 400 C" e 3oo u.. ..-LOO')('I')I"--..-LOO')('I')I"--..LOC.OI"--O')OC\.I('I')"
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Table A.13 Road 13 Summary Before Immediately After Mean 119.962 Mean 109.575 Mean 98.109 Standard Standard Standard Error 0.184 Error 0.111 Error 0.117 Median 122 Median 108 Median 99 Mode 123 Mode 108 Mode 99 Standard Standard Standard Deviation 19.274 Deviation 11.659 Deviation 12.325 Sample Sample Sample Variance 371.483 Variance 135.942 Variance 151.915 Kurtosis -0.384 Kurtosis 3.375 Kurtosis 3.760 Skewness 0.014 Skewness 0.815 Skewness 0.325 Range 127 Range 130 Range 147 Minimum 72 Minimum 67 Minimum 51 Maximum 199 Maximum 197 Maximum 198 Sum 1322216 Sum 1207732 Sum 1081360 Count 11022 Count 11022 Count 11022 Confidence Confidence Confidence Level(95.0%) 0.360 Level(95.0%) 0.218 Level(95.0%) 0.230 83

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Chart A.40 Road 14 Histogram Before Data 200 180 160 >. 140 0 120 S:::: G) 100 :::l C"' G) 80 .... LL 60 40 20 0 Road 14 Histogram Before Data ...--0)!'--l{)C"')...--CJ)!'--l{)C"') "
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Chart A.41 Road 14 Histogram Immediately After Data Road 14-Histogram Short-Term Data 250 120.00% 200 100.00% 80.00% 0 150 c Frequency C1) 60.00% :::l C" -Cumulative % C1) 100 L.. LL 40.00%. 50 20.00% Speed (kph) ss

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Chart A.42 Road 14 Histogram After Data Road 14-Histogram Long-Term Data 300 250 200 u 1: Q) 150 ::::J C"' u.. 100 50 0 I'-. ....... co 1.0 C'\1 0'> co ('") "<:t"<:ti.OCOCOJ'-..COCOO'>O Speed (kph) 120.00% 100.00% 80.00% 60.00% lr'<'l'-ff{il Frequency % 40.00% 20.00% 86

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Table A.14 Road 14 Summary Before Immediately After Mean .592 Mean 75.095 Mean 73.332 Standard Standard Standard Error 0.197 Error 0.179 Error 0.182 Median 81 Median 77 Median 76' Mode 85 Mode 77 Mode 76 Standard Standard Standard Deviation Deviation 9.854 Deviation 10.042 Sample Sample Sample Variance 117.389 Variance 97.094 Variance 100.852 Kurtosis 1.981 Kurtosis 1.514 Kurtosis 0.608 Skewness -0.964 Skewness -0.844 Skewness -0.755 Range 76 Range 66 Range 64 Minimum 41 Minimum 40 Minimum 40 Maximum 117 Maximum 106 Maximum 104 Sum 241164 Sum 227538 Sum 222196 Count 3030 Count 3030 Count 3030 Confidence Confidence Confidence Level(95.0%) 0.386 Level(95.0%) 0.351 Level(95.0%) 0.358 87

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Chart A.43 Road 15 Histogram Before Data 200 r:::: 150 Cl) :::J C" f! 100 LL 50 Road 15 Histogram Before Data 100.00% 80.00% 60.00% 40.00% 20.00% .00% Speed (kph) 88 Frequency --u-Cumulative%

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Chart A.44 Road 15 Histogram Immediately After Data Road 15 -Histogram Short-Term Data 300 120.00% 250 100.00% >-200 (,) 80.00% c Cl) 150 60.00% j % C'" .f 100 40".00% LL 20.00% .00% Speed (kph) 89

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Chart AA5 Road 15 Histogram After Data Road 15 Histogram LongTerm Data 350 .-------------r 120.00% 300 100.00% 250 80.00% 200 ::::J 60.00% C" 150 u.. 100 40.00% 50 20.00% 0 .00% Speed (kph) 90 !:M;;rx! Frequency %

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Table A.15 Road 15 Summary Before Immediately After Mean 83.653 Mean 79.411 Mean 75.818 Standard Standard Standard Error 0.162 Error 0.142 Error 0.137 Median 84 Median 80 Median 77 Mode 81 Mode 82 Mode 78 Standard Standard Standard Deviation 9.438 Deviation 8.284 Deviation 7.971 Sample Sample Sample Variance 89.069 Variance 68.618 Variance 63.537 Kurtosis -0.253 Kurtosis -0.144 Kurtosis 0.338 Skewness -0.404 Skewness -0.474 Skewness -0.421 Range 67 Range 59 Range 58 Minimum 41 Minimum 42 Minimum 40 Maximum 108 Maximum 101 Maximum 98 Sum 284671 Sum 270237 Sum 258010 Count 3403 Count 3403 Count 3403 Confidence Confidence Confidence Level(95.0%} 0.317 Leve1(95.0%} 0.278 Leve1(95.0%} 0.268 91

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. BIBILIOGRAPHY Anderson, R. (2000). Introducing speed cameras in the ACT-How to win friends and influence people. Proceedings of the 2000 Road Safety Research, Policing, and Education Conference. ARRB Transport Research (2000). Evaluation of the effectiveness of speed cameras in the ACT (Second Progress Report). Melbourne: ARRB Transport Research Ltd. ARRB Transport Research (2001). Evaluation of the effectiveness of speed cameras in the ACT (ARRB Final Report). Melbourne: ARRB Transport Research Ltd. Blackburn, R.R. and Gilbert, D.T. (1995). Photographic enforcement of traffic laws -A synthesis of highway practice. Synthesis of Highway Practice (p. 219). Washington, D.C.: National Academy Press. Cameron, M., Cavallo, A. and Gilbert, A. (1992). Crash-based evaluation of speed camera program in Victoria 1990-1991 (Report No. 42). Melbourne: Monash University Accident Research Centre. Cameron, M., Newstead, S., Diamantopoulou, K. and Oxley, P. (2003). The interaction between speed camera enforcement and speed-related mass media publicity in Victoria (Report No. 201). Melbourne: Monash University Accident Research Centre. Coleman,J.A. et al. (1996). FHWA study tour for speed management and enforcement technology (FHWA-PL-96-006). Washington, D.C.: US Department of Transportation. Corbett, C. and Simon, F. (1999). The effects of speed cameras: how drivers respond (Road Safety Research Report 11). London: Development of Environment, Transport and the Regions. Cowley, J .E. (1980). A review of rural speed limits in Australia (Report CR 20). Canberra: Office of Road Safety, Department ofTransport. Cowley, J.E. (1983). Characteristics of the speeding driver (Report No. 8/83 [GR]). Hawthorn, Victoria: Road Traffic Authority. Cowley,J.E. (1987). The relationship between speed and accidents: A literature review (Report No. 2/87 [GR]. Hawthorn, Victoria: Road Traffic Authority. 92

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Dublin, An Foras (1982). Effects of speed limits. Proceedings of the International Symposium on the Effects of Speed Limits on Traffic Accidents and Fuel Consumption. Fildes, B. (1995). Driver behaviour and road safety. In Brewer, N. and Wilson, C. (Eds.), P[Jcholo.f!J and Pofidng (pp 31-61). New Jersey: L.Erlbaum Association. Fildes; B.N. and Lee, S. (1993). The speed review: road environment. speed limits. enforcement and crashes (Consultant Report CR/93/CR 3/93A [RSB]). Canberra, Australia: Monash University Accident Research Centre for Federal Office of Road Safety, Road Safety Bureau, Roads and Traffic Authority of New South Wales. Finch, D.J., Kompfner, P., Lockwood, C.R. and Maycock, G. (1994). Speed. speed limits, and accidents (Project Report 58). Crawthome: Transport Research Laboratory (TRL). Freedman, M., Williams, A. and Lund, A. (1990). Public opinion regarding photo radar (Transportation Research Board no. 1270 Safety Research). Washington, D.C.: Accidents.Studies, Enforcement, EMS, Management, and Simulation, Transportation Research Board. French, D., West, R., Elander,J. and Wilding,). (1993). Decision-making style, driving style, and self-reported involvement in road traffic accidents. Ergonomics. 36, 6. Glad, A. and 0stvik, E. (1991). Automatic traffic surveillance in Telemark, NorwayEffects on driving speed and accidents Norwegian language with English summary (T0I report 87 /1991). Oslo: Institute of Transport Economics. Glauz, W.D. (1998). Review of automated technologies for speed management and enforcement. Managing speed. Revew of ccirrent practice for setting and enforcing speed limits (TRB special report 254). Washington, D.C.: National Academy Press. Gregersen, N. and Bjurulf, P. (1996). Young novice drivers: toward a model of their accident involvement. Accident Analysis and Prevention, 25, 4. Harrison, W.A., Fitzgerald, E.S., Pronk, N.J., and Fildes, B. (1998). An investigation of characteristics associated with driving speed (Report No. 140). Melbourne: Monash University Accident Research Hooke, A., Knox, J. and Portas, D. (1996). Cost benefit analysis of traffic light and speed cameras (Police Research Series Paper 20). London: Home Office Police Research Group. 93

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Insurance Institute for Highway Safety Status Report. (1998). Arlington, VA: Insurance Institute for Highway Safety. Lamm, R and Kloeckner, J .H. (1984). Increase of traffic safety by surveillance of speed limits with automatic radar devices on a dangerous section of a German autobahn -a long-term investigation. Washington D.C.: Transportation Research Board (IRB). Mackie, A. (1998). Urban speed management methods (TRL report 363). Crawthorne: Transportation Research Laboratory. Maycock, G. (1993). The use of cameras for the enforcement of speed limitsenhancing their effectiveness (TRL Report). Crawthorne: County Surveyors' Society. Ministry of Transportation of Ontario MTO (1997). Photo radar slows speeders Ontario publishes results of four-month preliminary study. Auto and Road User J ourn!!l, 3, 97. Nilsson, G. (1992). Trials with automatic speed surveillance 19901992 Swedish language with English summary (VII Report 378). Link6ping: Swedish Road and Traffic Research Institute. Portans, I. (1988). The potential value of speed cameras (Report no. 2/88 [SR]). Hawthorne, Victoria: Road Traffic Authority. Sanderson,J.T. and Cameron, M.H. (1982). Speed control. Victoria: Traffic & Safety Department, Royal Automobile Club of Victoria (RACV) Ltd. Social Development Committee (1991). Report upon the inquiry into speed limits in Victoria. Melbounl.e, Victoria: Parliament of Victoria, Government Printer. Taylor, M. (2001). The speeds of vehicles which are involved in fatal accidents. Traffic Engineering and Control42, 2. Taylor, M., Baruya, A. and Kennedy,]. (2002). The relationship between speed and accidents on rural single-carriageway roads (TRL Report 511). Crawthorne: Transportation Research Laboratory. Taylor, M., Lynam, D. and Baruya, A. (2000). The effects of drivers' speed on the frequency of road accidents (Transport Research Laboratory Report 421). Crawthome: Transportation Research Laboratory. 94

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Toogood,). (1993). A road safety success story. Milestones (pp. 32-36), Winter. Webster, D. and Wells, P. (2000). The characteristics of speeders (TRL report 440). Crawthorne: Transportation Research Laboratory. Zaidel, D. and Makinen, T. (1999). Automation concepts and technologies for improving law enforcement (Research report 482). Espoo: VTT Communities and Infrastructure. --95