Citation |

- Permanent Link:
- http://digital.auraria.edu/AA00002266/00001
## Material Information- Title:
- Modeling truck accidents at highway interchanges prediction models using both conventional and artificial intelligence approaches regression, neural networks, and fuzzy logic
- Creator:
- Awad, Wael Hassan
- Publication Date:
- 1997
- Language:
- English
- Physical Description:
- xiii, 241 leaves (some folded) : illustrations ; 29 cm
## Subjects- Subjects / Keywords:
- Truck accidents ( lcsh )
Truck accidents ( fast ) - Genre:
- bibliography ( marcgt )
theses ( marcgt ) non-fiction ( marcgt )
## Notes- Bibliography:
- Includes bibliographical references (leaves 237-241).
- General Note:
- Submitted in partial fulfillment of the requirements for the degree, Doctor of Philosophy, Civil Engineering.
- General Note:
- Department of Civil Engineering
- Statement of Responsibility:
- by Wael Hassan Awad.
## Record Information- 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:
- 37822191 ( OCLC )
ocm37822191 - Classification:
- LD1190.E53 1997d .A83 ( lcc )
## Auraria Membership |

Full Text |

MODELING TRUCK ACCIDENTS AT HIGHWAY INTERCHANGES
PREDICTION MODELS USING BOTH CONVENTIONAL AND ARTIFICIAL INTELLIGENCE APPROACHES REGRESSION, NEURAL NETWORKS, AND FUZZY LOGIC by WAEL HASSAN AWAD BS, University of Jordan, 1986 MS, University of Colorado at Denver, 1991 A thesis submitted to the University of Colorado at Denver in partial fulfillment of the requirements for the degree of Doctor of Philosophy Civil Engineering 1997 1997 by Wael Hassan Awad All rights reserved. n This thesis for the Doctor of Philosophy degree by Wael Hassan Awad has been approved by Bruce N. Janson Dan M. Frangopol Sarosh I. Khan 3/3/hr a Date A wad, Wael Hassan (Ph.D., Civil Engineering) Modeling Truck Accidents at Highway Interchanges Prediction Models Using Both Conventional and Artificial Intelligence Approaches: Regression, Neural Networks, and Fuzzy Logic Thesis directed by Associate Professor Bruce N. Janson ABSTRACT Large trucks represent a significant proportion of overall vehicle volumes on the nation's highways, and this proportion is increasing at the same time that larger and longer trucks are being used. Highway geometric design elements, including interchange configurations and ramp characteristics, contribute significantly to traffic accidents that involve trucks. However, this contribution is very difficult to quantify, because of the confounding influence of other factors, such as human behavior, traffic conditions, and prevailing weather conditions. Most previous accident studies used regression analysis to develop equations to explain accident rates. All previous attempts have had mixed results, and no set of geometric/accident relationships is widely accepted. Deficiencies of such models were attributed to different factors, such as quality and quantity of accident data and statistical methods used for prediction. IV Accident reporting systems in most states compile information about many variables that contribute to accident causation in a non consistent way. For example, for two different accidents, "wet surface" can be a contributing factor to one accident, but only a neutral factor in another accident. Existing accident reporting systems do not solve this problem. In this study, different approaches were applied to explain truck accidents at interchanges in Washington state during the period from 1/1/1993 to 3/3/1995. Three models for each ramp type were developed using linear regression, neural networks, and a hybrid system using fuzzy logic and neural networks. The study showed that linear regression was only able to predict accident frequencies that fell within one standard deviation from the overall mean of the dependent variable. However, the coefficient of determination was very low in all cases. The other two artificial intelligence (AI) approaches showed a high level of performance in identifying different patterns of accidents in the training data, and presented a better fit when compared to the linear regression. However, the ability of these models to predict test data that was not included in the training process showed unsatisfactory results. The results suggest that AI approaches are promising tools for exploring the problem, but that the data have many deficiencies. This abstract accurately represents the content of the candidates thesis. I recommend its publication. Signed* Bruce W. Janson 'DecUaztim *] dedicate tArie t&ecic to mtf, puvieute, cut^e, {pwtily, and fyUenoU, {jW tAeir inceecant encouna/^ement and patience tAxouyAout tAe fraet yeane. Specifically, *1 devote tAio tuonA to uncle /tcoad and Aic wife. rfdwotijÂ£eclcymettt& On the name ofa Adah, moot ynacioue, moot mencifaul 'Zt/e taiee to decree ofa wiodom whom TVe pleaee: hut oven all endowed with hnowledye ie one, the atl-huowiny OÂ¥oly tZunau s. 12/1.76 0 ant deeply indebted to the almiyhty Adah faon hie mency upon me, and to my panente faon thein tove and euppont. 0 ynatefaully achuowledye the aeeiotance ofa my adoieon, *Dn. "Siucc Ot. fyaneon. and my committee memhenc: *Dn. tyamee 'Diehmann; *Dn. *Dan 'pxanyopol; *Dn, Aynn tyohtneon; and *Dn, Sanoeh 'Khan faon thein intiyht and oaluaMe comments. Ado. 0 would (the to thanh both *Dn. 'Zl/illiam 'ZVolfae and *D%. tyamee Koehlen faon thein euyyeotione. \finally, 0 wieh to expneee my appneciation to OKathwonhe One. faon pmooidiny me with OZiAHAA^ eofatwane. Spzeeial thanhe yo to thein technical euppont enyineene faon thein eenoice. TUael Awad CONTENTS Tables ................................................................x Figures ..............................................................xi CHAPTER 1. INTRODUCTION .......................................................1 Background ....................................................1 Problem Statement and Research Objectives .....................6 Review of Relevant Research ...................................7 Traffic Accidents .......................................8 Interchange Safety .....................................10 Truck Accidents ........................................10 Truck Accidents at Interchanges ........................12 Truck Exposure Measures ................................13 Neural Network .........................................15 Fuzzy Logic ............................................20 2. DATA ACQUISITION AND PRELIMINARY ANALYSIS ........................23 Data Inventory ...............................................23 Data Manipulation and Preparation ............................26 Quality of Data ..............................................29 Preliminary Observations from Washington State Truck Accidents Data .........................................................34 Factor Analysis and Variable Selection .......................37 viii 3. MODELS FORMULATION .............................................40 Conventional Multiple Linear Regression Model ...............44 Intrinsically Linear Regression Model .......................45 Neural Network Model ........................................47 ANFIS Model .................................................51 ANFIS Architecture .....................................52 Model Formulation ......................................56 4. MODELS DEVELOPMENT .............................................59 Conventional Multiple Linear Regression Model ...............59 Intrinsically Linear Regression Model .......................60 Neural Network Model ........................................72 ANFIS Models ................................................84 5. RESULTS AND FINDINGS ...........................................97 Multiple Linear and Intrinsically Linear Regression Models ..97 Neural Network Model .......................................100 ANFIS Models ...............................................102 Sensitivity Analysis for Neural Networks ...................103 Sensitivity Analysis for ANFIS Model .......................106 Evaluation of Models .......................................106 6. CONCLUSIONS, RECOMMENDATIONS, AND FUTURE WORK ................Ill General Findings and Observations ..........................112 Recommendations for Future Work ............................115 APPENDIX ..........................................................118 A. Traffic and Truck Growth Statistics .....................119 B. Washington State Data ...................................122 C. Possibility Theory and Fuzzy Logic ......................129 IX D. Linear Regression Models ..........................144 E. Neural Network Models .............................158 F. ANFIS Models ......................................171 G. All Models Comparison .............................232 BIBLIOGRAPHY ...............................................237 x TABLES Table 2.1: Comparison of Truck Accidents per Year in Three States by Ramp Type 25 2.2: Washington Truck Accidents on All Ramps ............................. 31 2.3: Washington Truck Accidents By Ramp Type ............................. 32 2.4: Washington Truck Accidents by Ramp Type, Conflict Area .............. 34 2.5: Washington Truck Accidents per RTADT by Ramp Type, Conflict Area .... 35 2.6: Washington Truck Accidents per RTVMT by Ramp Type, Conflict Area .... 36 3.1: Training and Checking Data Size ..................................... 43 4.1: Multi-Linear Regression Models ...................................... 60 4.2: Intrinsically Multi-Linear Regression Model ......................... 61 4.3: Linear Regression RMSE per Ramp Type ................................ 62 4.4: Mean and Standard Deviation per Ramp Type ........................... 63 4.5: Neural Network RSME per Ramp Type With Four Inputs .................. 73 4.6: ANFIS Minimum RMSE .................................................. 86 4.7: ANFIS RSME per Ramp Type ............................................ 87 5.1: Sensitivity Analysis for Neural Network Model....................... 105 5.2: Sensitivity Analysis for ANFIS Model ............................... 107 5.3: Training Data RMSE by Ramp Type by Modeling Method ................. 108 5.4: Checking Data RMSE by Ramp Type by Modeling Method ................. 110 XI CO FIGURES Figure 1.1 : Multiple Layer Neural Network........................................ 16 1.2 : Multiple-Input Neuron ............................................... 18 2.1 : Ramp Conflict Areas ................................................. 26 2.2. a: Truck Accident Distribution Upstream And Downstream of On-Ramps .... 27 2.2. b: Truck Accident Distribution Upstream And Downstream of Off-Ramps .... 28 2.3 : Distribution of Ramps in Washington State by TAF During 27 Months ... 33 2.4 : The Variables Considered in The Modeling From Washington State Database ............................................................. 39 3.1 : The Methodology for Prediction Models ............................... 41 3.2 : Training and Checking Data by Ramp Type in Washington State ......... 43 3.3 : Two-layer Network (Input-Hidden-Output) ............................. 47 3.4 : Log-sigmoid Transfer Function ........................................48 3.5 : ANFIS Output Function for Sugeno Type 1 Model ........................51 3.6 : Sugeno-type Systems ..................................................52 .7 : Fuzzy Reasoning for Sugeno Type 1 model ...............................53 3.8 : ANFIS Architecture for Sugeno Type 1 model ...........................54 4.1 .a: Diamond Ramps Linear Regression Model (Training Data)............... 64 4.1 .b: Loop Ramps Linear Regression Model (Training Data).................. 65 4.1 .c: Outer Connector Ramps Linear Regression Model (Training Data)....... 66 4.1 .d: Directional Ramps Linear Regression Model (Training Data)........... 67 4.2. a: Diamond Ramps Linear Regression Model (Checking Data)............... 68 4.2. b: Loop Ramps Linear Regression Model (Checking Data).................. 69 4.2. c: Outer Connector Ramps Linear Regression Model (Checking Data)....... 70 4.2. d: Directional Ramps Linear Regression Model (Checking Data)........... 71 xii Figures Cont. 4.3 : Directional Ramps Neural Network Model (Training and SSE) With Six Inputs...................................................... 73 4.4. a: Diamond Ramps Neural Network Model With Four Inputs ................75 4.4. b: Loop Ramps Neural Network Model With Four Inputs .................. 76 4.4. c: Outer Connector Ramps Neural Network Model With Four Inputs ....... 77 4.4. d: Directional Ramps Neural Network Model With Four Inputs ........... 78 4.5. a: Diamond Ramps Neural Network Model (Checking data) ................ 80 4.5. b: Loop Ramps Neural Network Model (Checking data) ................... 81 4.5. c: Outer Connector Ramps Neural Network Model (Checking data) ........ 82 4.5. d: Directional Ramps Neural Network Model (Checking data) ............ 83 4.6 : ANFIS Model ....................................................... 85 4.7. a: Diamond Ramps ANFIS (Training Set 1) .............................. 88 4.7. b: Loop Ramps ANFIS Model (Training Set 1) ........................... 89 4.7. c: Outer Connector Ramps ANFIS Model (Training Set 1) ................ 90 4.7. d: Directional Ramps ANFIS Model (Training Set 1) .................... 91 4.8. a: Diamond Ramps ANFIS (Checking Set 1) .............................. 93 4.8. b: Loop Ramps ANFIS Model (Checking Set 1) ........................... 94 4.8. c: Outer Connector Ramps ANFIS Model (Checking Set 1) ................ 95 4.8. d: Directional Ramps ANFIS Model (Checking Set 1) .................... 96 5.1 : Multi-Linear Regression Model for Directional Ramps ................. 99 xiii CHAPTER 1 INTRODUCTION Background Large trucks represent a significant proportion of the overall vehicle flow on the nation's highways. National statistics show that this proportion is increasing at the same time that larger and longer trucks are being used. This has been noted particularly since the Surface Transportation Assistance Act (STAA) was passed in 1982. The STAA minimized restrictions on the size and weight of trucks allowed on federal highways. The Tandem Truck Safety Act (TTSA), which followed in 1984, allowed yet greater access for large vehicles to the highway system. Trucks provide customers with more flexible service than other freight transportation modes by offering door-to-door service. Thus, the demand for this service is expected to keep increasing. The average annual increase in truck registrations is three percent, or approximately four million new trucks entering the fleet each year. According to the Transportation Statistics Annual Report 1994: Trucks and buses account for over one-forth of all vehicle miles of travel in the U.S. Trucks with six or more tires, ranging from local delivery vehicles to combination trucks with three trailers, account for less than 10 percent of total vehicle miles of travel. 1 In terms of freight movement, the same report says: Trucking has shown slow but continuous gains in modal share since 1950. Overall shares were about 16 percent in 1950; they reached 20 percent by 1960 and 25 percent in the mid-1980s. Present shares are in the range of 26- 27 percent of ton-miles, and 79 percent of the overall freight movement revenue.(Appendix A, Figures 1, 2) An interchange is a system of interconnecting roadways that provides for movements between two or more grade-separated highways. The connection for traffic flow between one roadway and the other is called a ramp. Many ramp types and different interchange configurations could be selected during the design process, depending on a variety of considerations, including safety, capacity, and roadway- functions. The ramp dimensions and design configurations of many existing interchanges were designed to accommodate small vehicles (passenger cars, vans, and pickups), rather than large ones. In general, the interstate systems that exist today have been built with the ultimate geometric standards. However, the federally-aided primary and secondary systems in many instances include below-standard geometric designs, which are critical to the safety of large-truck operations. Many highway safety engineers and researchers believe that the combination of increased use of these ramps by large trucks and the fact that the ramps were not designed to meet large-vehicle requirements, are the major reasons for the increasing number of accidents involving large vehicles on ramps. Nationally, 20 percent of truck accidents occur at interchanges. In Colorado, this percentage increased from about 23.6 percent in 1991 to 30.9 percent in 1993. During the period from 1/1/1993 to 3/31/1995, the truck accident record for Washington state revealed that about 41 2 percent of the truck accidents on the Washington state highway system occurred within the interchange influence areas. The 0.5 % fatality rate for accidents that involve trucks is higher than the comparable 0.3 % rate for accidents involving small vehicles. The injury rates were 39 % and 54 %, respectively. The economic consequences of truck accidents, according to a study by Bowman, B. and Lum, H. ,1990 could be summarized as follows: " An estimate of the total annual cost of urban freeway truck accidents was determined to be $634,000 per freeway mile. This cost consisted of accident costs of $182,000, delay costs of $440,000, clean-up costs of $3,000, and operating costs of $9,000 per freeway mile. Expanding this estimate to the 1,937 interstate and 560 freeway miles in the United States with average daily traffic volumes of over 100,000 vehicles results in a nationwide annual cost of $1.6 billion." highway geometric design elements, including interchange configurations and ramp characteristics, contribute significantly to traffic accidents, especially where large vehicles such as trucks are concerned. However, this effect is very difficult to quantify because of the confounding influences of other factors. Those factors include human behavior, the prevailing environmental conditions, the amount of traffic, and the operational characteristics of the large vehicles themselves. To establish a relationship between traffic accidents and the factors involved, high quality data is required, as well as the use of good statistical methods that overcome the problems and deficiencies inherent in previous models. The data compiled for most accident reporting systems are typically produced by police accident investigations. Police officers usually have neither the time nor the experience to conduct in-depth accident investigations or collect the necessary data. 3 Also, it is not always practical to investigate fully each accident. Doing so might delay traffic and require a fully trained crew for investigations. Many state departments of transportation are already in the process of improving the quality of their accident reporting systems, and of attempting to include in them other factors related to the vehicle, driver, road, traffic, and weather conditions prior to and after an accident takes place. Washington state was one of the leading states in this area. The Washington traffic accident file has about 90 fields of information for each recorded accident, ranging from time and date of accident to pedestrian information. It also includes other data related to location, driver, vehicle, and the road (Appendix B). Four other sets of data in a hard copy format were supplemented with the above Washington accident file (Appendix B). The first data set contains drawings (not to scale) for all the interchanges in the Washington highway system (about 500 locations connecting some 7000 miles of road). Each drawing shows the milepost of the gore and beginning of the deceleration lane, or the end of the acceleration lane for each ramp (taper). The second data set is the annual traffic report for years 1992 to 1994. The information included is the Annual Average Daily Traffic (AADT) volume for the main lane of state routes at different major crossroads (interchanges), and some truck percentages at selected locations. The third data set includes traffic counts for most of the ramps (about 75 % of them) and less than 10 % of the truck percentages at such ramps. The fourth data set has ramp information, such as ramp length, number of lanes, and shoulder information. 4 Despite the fact that Washington data is the most comprehensive recorded data in the nation, it remains uncertain and ambiguous in regard to human error (in its reporting and coding), and in regard to the fact that a period of time often elapses between the accident and the reporting time. Therefore, the uncertainty of data has to be considered in the modeling process, with this uncertainty related to the criterion variable as well as the explanatory variables used in the prediction models. However, in this study, we still acknowledge that the main deficiency in the existing traffic accident databases-including Washington state database-was not treated yet. This deficiency is related to the nature of the recorded variables into the accident report, and it is partially behind the unsatisfactory results of previous prediction models. The existing accident reporting systems nationwide have partial information about each recorded accident, the reported variables are related to the prevailing conditions at the time of the accident regardless whether these variables are contributing to the causation of the accident or not. A complete accident information database should distinguish among variables in terms of their level of contribution to the accident causation, and should lead to better prediction results. Statistically, it is not difficult to find a relationship among randomly generated variables. In a simulation study, the R2 for a regression model with 50 variables randomly generated from a standard normal distribution was 0.59. Therefore, using meaningful variables is very significant in any prediction model. 5 In our study, different approaches will be examined in an attempt to study truck accidents at interchanges. A traditional regression model will be built and compared to other models that will employ some Artificial Intelligence techniques such as neural network and fuzzy logic. The combination of the neural networks and fuzzy logic is called a hybrid system. The proposed hybrid model will use an Adaptive-Network-Based Fuzzy Inference System (ANFIS). The goal behind using new techniques, such as neural network and fuzzy logic, is to evaluate their performance, and compare them to the traditional regression methods. The developed models will be used as pilots for future work in order to improve the prediction models. However, sample size, and software and hardware limitations will be obstacles affecting the quality of the initial models. Problem Statement and Research Objectives Traffic accidents are generally caused by factors and conditions to which the driver and the vehicle are subjected, and which reflect negatively on driving behavior. Despite the fact that many researchers have tried to resolve this problem, they agree on few common factors, and the relationship among them has not yet been well determined. As part of the problem, truck accidents within the highway interchange areas are considered unique. This specific problem is even more complicated, because of the integration in effect of the interchange geometric characteristics and the turbulence of the traffic flow that enters and exits the main road. In this regard, one 6 must keep in mind the different operating needs of drivers and the dimensional characteristics of small vehicles and of large vehicles on ramps. Thus, the main goal of this study is to evaluate artificial intelligence techniques as alternative tools in modeling traffic accidents, and to assess the limitations and capabilities of such tools, so as to improve similar models in the future. These goals will be achieved by developing prediction models for truck accidents on different ramp types. They will predict the number of truck accidents that occurred at each ramp in Washington state during the 27 month period. These prediction models will establish a relationship between the frequency of truck accidents at each ramp and selected variables. The selected variables represent different characteristics related to the ramp geometry, traffic conditions and other environmental variables. Exploring such relationships should enable us to devise counter measures for the broad problem, and these counter measures can be the keystone for a better and safer highway system. As part of the procedure, we will construct different models and outline the merits and deficiencies of each one of them. This will lead to final observations, recommendations and suggestions for further future work. Review of Relevant Research This section includes a review of previous work and theory related to truck accidents and artificial intelligence techniques. This review is presented in the following order: traffic accidents in general; truck accidents on segments of highway; 7 truck accidents at interchanges; traffic exposure measures; the neural networks; and fuzzy logic. Traffic Accidents The relationships between traffic accidents and highway geometric variables, such as horizontal curvature, vertical curvature and grade, lane width and shoulder width, have been the subject of many studies (e.g., Roy Jorgensen Associates, Inc., 1978; Zegeer et al., 1987; Okamoto and Koshi, 1989; Miaou et ah, 1991; Miaou and Lum, 1993). These studies employed different statistical models to investigate the safety issue at a given section of highway with specified attributes and geometric variables. Most of these statistical models were developed using either conventional linear regression (Jovanis and Chang, 1986; Saccomanno and Buyco, 1988; Miao, 1995), or Poisson and Negative Binomial regression {Joshua and Garber, 1990; Miaou et al., 1991; Shankar et al., 1994; Hadi et al, 1995; Miao, 1995; andPoch and Mannering, 1996). The results of the conventional linear regression models show unsatisfactory statistical properties for explaining the traffic-accident phenomenon. In addition, the application of the Poisson and negative binomial models had limitations. All previous attempts to predict and explain the nature of traffic accidents and their connections to geometric characteristics have had mixed results. Thus no set of geometric-accident relationships is widely accepted. The deficiencies of such models were attributed to such factors as: 8 1. The quality of accident data used in the model: The data compiled for most accident-reporting systems are typically produced by police accident investigations. Police officers usually have neither the time nor the experience to conduct in-depth accident investigations, nor to collect the necessary data. In addition, it is not practical for them to investigate fully each accident. 2. The quality of the accident report: The type of data compiled for most accident-reporting systems does not explain why the accident happened. Moreover, many of these reports have conflicting statements. A number of states are currently working to modify and enhance the structures of their accident-reporting systems. 3. Statistical tools: Traffic accidents are random discrete events. The cause of these events is related to the different factors mentioned above, and assumes that each variable (factor) is independent which is far from the reality. Most previous linear regression models assumed that accident frequency could be represented by a continuous distribution function, others used Poisson or Negative Binomial regression, which use a discrete distribution function. In any case, priori knowledge of the distribution function is only an assumption based on approximation. It appears that this assumption is behind the failure of most of these models. Another reason could be the assumption of independence among the contributing accident variables. Also, these statistical models suffered from some inherited problems. Among them were uncertainty of independent variables (regression models assume no measurement errors in these variables); ignoring the variation in characteristics between different locations within the same category, and even for the same location during different periods of time; and, finally, the omitted variables that might have 9 been excluded from the model. These could have been a significant reason for poor fit. Interchange Safety Different studies indicate that the design of certain types of ramps such as cloverleaf ramps, scissor ramps, and left-side ramps should be avoided where possible. In addition, according to Twomey et al. 1993, the potential for accidents at interchanges has been related to ramp-traffic volume, main-road traffic volume, and spacing between interchanges. This study also concluded that rehabilitation of ramps is effective in reducing accident experience. Truck Accidents Since 1970, several reports and studies have been published related to truck accidents and safety. Some of these reports were limited to studying truck accidents on highways with different functional classes; accident rates of different types and degrees of severity; relationships between vehicle configuration and accidents; truck accidents at interchanges, ramps, and work zones; and other truck-safety aspects. Recent studies attempt to relate truck accident rates to various traffic and geometric variables. According to the model produced by Joshua and Garber, 1990, the significant geometric design variables were slope change rate for primary highways and curvature change rate for freeway type facilities. The major limitations 10 of this study were the small sample size, failure to consider other factors (driver, environment), and not distinguishing among various truck types. A study by Chatterjee et al., 1994 revealed that the human factor, especially driver failure due to fatigue and inadequate training, contributes heavily to truck accidents. These findings were discovered during many focus-group discussions with truck drivers and could not be drawn from accident reports alone. The impact on the design criteria of substantial increases in truck weights and dimensions in the past few decades was studied by Hutchinson, B., 1990. Hutchinson's study concluded that many infrastructure design procedures should be revised to incorporate the new operational needs for trucks based on current truck dimensions and weights. The operational effects of larger trucks on rural roadways was also the subject a study by Zegeer et al, 1990. The results showed that driver behavior and site differences have more of an effect on vehicle operations than the effect of different truck types. Yet another study, by Miaou et al., 1993, employed different regression models to establish empirical relationships between truck accidents and highway geometric design. This study suggested areas in which the quality and quantity of data could be enhanced to improve the developed model, such as by including detailed truck exposure data. So, despite the limitations of the data used, some encouraging relationships were developed. 11 Truck Accidents at Interchanges None of the above work focused specifically on truck accidents at interchanges. Further, the literature reveals few studies besides that of Ervin et al., 1986, directed at investigating this problem. The Ervin study focused on the problem of truck loss-of-control accidents on interchange ramps, from the viewpoint of the suitability of highway geometric design. The results show that various aspects of the geometric standards (unchanged in more than 30 years) provided by the American Association of State Highway and Transportation Officials (AASHTO) offer a slim margin of safety for trucks, especially on exit ramps. Some AASHTO recommendations were written to educate truck drivers about locations with potential risk. A study conducted by Garber et al., 1992, attempted to identify the characteristics of large-truck accidents on highway ramps in Virginia. As Garber claimed, few studies were conducted after 1983 related to this topic, and none of them explored in detail the relationship between truck accidents and highway interchanges. The Garber study was limited to performing proportionality tests that compared the percentage of truck-accident involvement by ramp type, collision type, highway type, and severity. An important finding was that the truck-accident involvement ratio (number of truck accidents on a ramp per total number of accidents on the road section in which the ramp is located) increases as the difference between the average speed of the approaching truck and the posted speed limit on the ramp increases. Also, as the ramp radius increases, that ratio decreases. 12 Another study, conducted by Leonard, J., 1992, focused on large-truck crashes on freeway-to-freeway connectors, and attempted to simulate the dynamic response of heavy vehicles at ramps. Basically, it was a single-vehicle-roadway- driver model to determine the rollover threshold. A linear regression model to predict the failure speed of large trucks was used, and the results showed that the failure speed ranged from 12-to-21 miles per hour above design speed limit. Truck Exposure Measures In accident analysis, the exposure is a technique based on the opportunity for interaction among vehicles and used to compute accident rates. So far, researchers have developed different concepts and used different methods to measure traffic exposure. The simplest form of exposure can be measured by Vehicle Miles of Travel (VMT) generated during a specific period over a certain road section. According to Khasnabis and Al-Assar, 1989, the purpose of measuring exposure is to enable the analyst a reasonable assessment of "accident risk." Because of the complexity of this topic, it is still subject to a continuous debate and future research. It has been discussed in detail by Thorpe, 1964, Haight, 1970, and Hauer, 1982. The most common measure used to determine accident rate is: Accident rate = Number of accidents / VMT This measure is appropriate if used for all accidents in general, but it is hard to justify if used for subgroups of the traffic stream. However, different approaches have been adopted in such cases. In truck accident cases, Khasnabis and Al-Assar, 1989 13 explained three approaches to calculate the truck accident rate, and proposed the following approach: Truck accident rate = Number of accidents involving trucks / [Truck VMT Factor] The Factor in the above equation is the ratio of number of trucks involved in truck accidents to the number of all vehicles involved in the same truck accidents. This approach recognizes the rule of the traffic volumes of all vehicles during truck accidents, not only the truck volume in the traffic stream. However, for considerations of simplicity, it has been decided to model truck accident frequencies rather than truck accident rates. In our case, considering truck accidents at interchanges, we see that many accidents occurred at different segments of the ramp, and some of them were exposed to main road traffic during merging and diverging, while other accidents were exposed to the ramp traffic only. Also, modeling accident frequency rather than accident rate has some advantages, such as eliminating the redundancy and correlation between the dependent variable and the independent variables. The frequency is not derived from any other variable, while the accident rate is a function of other variables. The calculation of VMT is normally related to a distance, which is the length of the highway section under consideration. In our case, this term in not applicable and requires special treatment when used. 14 Neural Network The application of artificial intelligence, specifically neural networks and fuzzy set theory, in transportation is considered new. Much of the related work was included in two recent Transportation Research Record publications, TRB#1399 and #1497, in 1993 and 1995, respectively. According to a paper by Mark Dougherty, 1995, the interest in neural networks by transportation researchers grew dramatically in the early 90s. The attention of 52 studies was directed to the following subjects: driver behavior; parameter estimation; pavement maintenance; vehicle detection and classification; traffic pattern analysis; freight operations; traffic forecasting; transportation policy and economics; air transportation; maritime transportation; submarine transportation; metro operations; and traffic control. However, literature reviews revealed no studies applying Artificial Intelligence to understanding truck accidents. Neural networks are designed to develop a mathematical model that connects input parameters with solutions, without the need to define the model. Solutions are based on calculating the error, which is the sum of the square of the differences between the actual and the desired output data. Several iterations are required to reach the minimum error that is allowed, depending on the learning rate (weight adjustment). A basic neural network (Figure 1.1) consists of three layers of interconnected nodes called neurons. Input-layer neurons receive data from the user. Output-layer neurons send information to the user. Middle (hidden) layer neurons receive signals from all the neurons in the input layer, and have the option of sending signals to all the neurons in the output layer. Neural networks do not "learn" by 15 adding representations to their knowledge base; they learn by modifying their overall structure. The applications of neural networks may present certain difficulties. For example, if there is no initial knowledge, learning derives entirely from experience, the quality level of knowledge and the bias related to the design of the network, the Inputs Hidden Layer Output VI Y Figure 1.1: Multiple Layer Neural Network number of nodes at each layer, the patterns of connections, and so on. Nonetheless, neural networks outperformed current methods of analysis because they successfully: 1. Handle noisy or irregular data from the real world 2. Deal with the nonlinearity of real-world events 3. Quickly provide answers to complex issues 4. Are easily and quickly updated 5. Readily provide generalized solutions 6. Interpret information for large numbers of variables or parameters 16 Next, we will summarize the conceptual and theoretical math of neural networks, beginning with a description of the neuron models that form the basis of the neural networks. The following two general equations represent the basics of the neuron behavior and the relationship between the input and the output for each neuron: *=ZWX/ (L1) 7-1 T* -0*) (1.2) where xx,x2,---,Xj are the input signals; wkl,wk2,...,M'kj are the synaptic weights of neuron k; uk is the linear combiner output; 6k is the threshold;
activation function; yk is the output signal of the neuron. |