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Microscopic accident prediction models for signalized intersections Quintero Toscano, Mario Alberto 2000

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MICROSCOPIC ACCIDENT PREDICTION MODELS FOR SIGNALIZED INTERSECTIONS By  MARIO ALBERTO QUINTERO TOSCANO B.A.Sc. (Civil Engineering), Instituto Tecnologico y de Estudios Superiores de Monterrey, Monterrey, Mexico, 1997  A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in  THE FACULTY OF GRADUATE STUDIES DEPARTMENT OF CIVIL ENGINEERING  We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH COLUMBIA APRIL, 2 0 0 0 © Mario Alberto Quintero Toscano, 2 0 0 0  In presenting this thesis in partial fulfilment of the requirements for an advance degree at The University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that the permission for the extensive copying of this thesis for scholarly purposes may be granted by the Head of my Department or by his representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.  Department of Civil Engineering The University of British Columbia 2324 Main Mall Vancouver, B. C. Canada, V6T 1Z4  Date:  Mk-roseopk A c c k k i i t Prediction M o d e l s i ' w S i g n a l l e d I n k i ' s e c t i o n s  ABSTRACT The main objective of this thesis is to develop microscopic accident prediction models for estimating the safety potential of 4-leg signalised intersections in the City of Vancouver, B.C. and describes the applications of these models in traffic safety analysis. The aim, therefore, is to examine the traffic variables that appear to underlie the occurrence of accidents of these intersections and explain, in a statistical sense, the generation of accidents as a function of these variables.  Generalised linear regression was employed to  develop the models because of its superiority over conventional linear regression in modelling accident occurrence. The statistical software package GLIM4 was used to accomplish this task. The study made use of a sample of 8466 accidents that occurred at 170 4-leg signalised intersections during the years of 1994-1996.  The data on  accident frequencies and traffic volumes were obtained from the City of Vancouver. Several models that have different applications in the field of traffic safety were developed in this study for the 4-leg signalised intersections of the City of Vancouver. Different error structures that can be utilised to model the relationship between accidents and traffic flows are reviewed.  Microscopic  models for different accident types were developed in conjunction with macroscopic models for Total, Severe and Property Damage Only accidents. The microscopic models are presented in conjunction with the three macroscopic models which all resulted in statistical significance. Several model applications are discussed. Examples of how to obtain location-specific safety estimates, how to identify accident prone locations, how to rank the accident prone locations, and how to conduct a before and after safety evaluation are presented. Microscopic and macroscopic models are used simultaneously to determine which intersections should be regarded as accident  ii  M i c r o s c o p i c A c c i d e n t Prediction M o d e l s 1'ttr Signalised I n t e r s e c t i o n s  prone locations according to specific accident patterns that can be effectively treated by engineering countermeasures.  M i f r o s c o p k Aerifleur Prediction M o d e l s f o r Signalise si iotersecrkors  TABLE OF CONTENTS ABSTRACT TABLE OF CONTENTS  ii iv  LIST OF TABLES  vii  LIST OF FIGURES  ix  ACKNOWLEDGEMENTS  xi  1.  2.  INTRODUCTION  1  1.1 Background  1  1.2 Thesis Objectives  2  PREVIOUS WORK  3  2.1 Introduction  3  2.2 Conventional Linear Regression Models (CLRM)  3  2.3 Generalised Linear Regression Modelling (GLIM)  4  2.4 Microscopic Accident Prediction Models (APMs)  8  2.4.1 Model Evolution  9  2.4.2 Model Form  9  2.5 Outliers  10  2.6 Testing the Models Significance  12  M i c r o s c o p i c Aedficut Prediction M o d e l s for Signalise*! Intei'set'timis  3.  4.  5.  6.  2.7 The Empirical Bayes Refinement (EB)  13  2.8 Accident Prone Locations (APLs)  14  2.9 APMs for 4-leg signalised intersections  17  DATA DESCRIPTION  21  3.1 Introduction  21  3.2 Data Analysis  21  Models Development  34  4.1 The Models  34  4.2 Outlier Analysis  37  4.3 Testing the Models' Statistical Significance  37  Selection of APM type  40  5.1 Introduction  40  5.2 Poisson Model  40  5.3 Negative Binomial Model  42  5.4 Accident Database Evidence  45  APPLICATIONS  47  6.1 Introduction  47  6.2 Location specific prediction: EB safety estimates  47  iViiiToscofise A e c k k i s t P r e d i c t i o i s M o d e l s for Sisjmiissed I n l o t ' s e c i i o n s  7.  6.3 Identification of APLs  52  6.4 APLs Ranking  55  6.5 Critical Accident Frequency Curves  58  6.6 Before and After studies  71  6.7 Recommended Research  72  CONCLUSIONS  Bibliography  73 75  APPENDIX I  INTERSECTION LIST  79  APPENDIX II  DATA ANALYSIS  81  APPENDIX III  OUTLIERS ANALYSIS SAMPLES  88  APPENDIX IV  GLIM OUTPUT SAMPLE  91  APPENDIX V  STATISTICAL SIGNIFICANCE TESTS  95  vi  Microscopic Accident Prediction Models for StgnaJiscsl  loferswtKm:  LIST OF TABLES FIGURE 2.1  APL IDENTIFICATION PROCESS  16  FIGURE 3.1  TIME OF ACCIDENTS  24  FIGURE 3.2  1994  ACCIDENTS  25  FIGURE 3.3  1995  ACCIDENTS  25  FIGURE 3.4  1996  ACCIDENTS  26  FIGURE 3.5  T O T A L ACCIDENTS  26  FIGURE 3.6  3 YEARS ACCIDENTS PER MONTH  27  FIGURE 3.7  CONTRIBUTING CIRCUMSTANCES  28  FIGURE 3.8  T Y P E OF ACCIDENT  29  FIGURE 3.9  COMBINED T Y P E S  30  FIGURE 3.10  COMBINED T Y P E S P E R C E N T A G E S  30  FIGURE 3.11  ACCIDENT SEVERITY  31  FIGURE 3.12  ACCIDENT SEVERITY P E R C E N T A G E S  31  FIGURE 3.13  LIGHT CONDITION  32  FIGURE 3.14  ROAD CONDITION  32  FIGURE 3.15  W E A T H E R CONDITION  33  FIGURE 6.1  PREDICTED vs.  E B REFINED NUMBER OF ACCIDENTS  FIGURE 6.2  APL  FIGURE 6.3  CRITICAL C U R V E FOR AT1  59  FIGURE 6.4  CRITICAL C U R V E FOR AT5  60  FIGURE 6.5  CRITICAL C U R V E FOR AT7  61  FIGURE 6.6  CRITICAL C U R V E FOR AT13  62  FIGURE 6.7  CRITICAL C U R V E FOR LEFT TURN ACCIDENTS  63  FIGURE 6.8  CRITICAL C U R V E FOR RIGHT TURN ACCIDENTS  64  FIGURE 6.9  CRITICAL C U R V E FOR TOTAL ACCIDENTS  65  IDENTIFICATION PROCEDURE  51 54  FIGURE 6.10  CRITICAL C U R V E FOR S E V E R E ACCIDENTS  66  FIGURE 6.11  CRITICAL C U R V E FOR PDO  67  FIGURE 6.12  CRITICAL C U R V E S FOR VARIOUS K VALUES  ACCIDENTS  70  FIGURE AIII.1 C O O K ' S DISTANCES BEFORE REMOVALS  89  FIGURE AIII.2 C O O K ' S DISTANCES AFTER 2 WARRANTED AND 2 UNWARRANTED REMOVALS  89  FIGURE All.3 C O O K ' S DISTANCES BEFORE REMOVALS  90  FIGURE All 1.4 C O O K ' S DISTANCES AFTER 4 UNWARRANTED REMOVALS  90  FIGURE AV.1  96  FIGURE AV.2 FOR  AT 1  PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR AT 1  PREDICTED ACCIDENTS VS. A V E R A G E S Q U A R E D RESIDUALS AND ESTIMATED VARIANCE  96  Mimweopie  Aeefrleitt P r e d i c t i o n  MmM  •  i »  •  •  •  FIGURE A V . 3 PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR A T 5  97  FIGURE A V . 4 PREDICTED ACCIDENTS VS. A V E R A G E S Q U A R E D RESIDUALS AND ESTIMATED VARIANCE FOR A T 5  97  FIGURE A V . 5 PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR A T 7  98  FIGURE A V . 6 PREDICTED ACCIDENTS vs.  A V E R A G E S Q U A R E D RESIDUALS AND ESTIMATED VARIANCE  FOR A T 7 FIGURE A V . 7 PREDICTED ACCIDENTS vs.  98 PEARSON RESIDUALS FOR A T 13  99  FIGURE A V . 8 PREDICTED ACCIDENTS VS. A V E R A G E S Q U A R E D RESIDUALS AND ESTIMATED VARIANCE FOR A T 13 FIGURE A V . 9 PREDICTED ACCIDENTS vs.  99 PEARSON RESIDUALS FOR TOTAL ACCIDENTS  100  FIGURE A V . 1 0 PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE FOR TOTAL ACCIDENTS FIGURE A V . 1 1 PREDICTED ACCIDENTS vs.  100 PEARSON RESIDUALS FOR S E V E R E ACCIDENTS  101  FIGURE A V . 1 2 PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE FOR S E V E R E ACCIDENTS  101  FIGURE A V . 1 3 PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR P D O ACCIDENTS  102  FIGURE A V . 1 4 PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE FOR P D O ACCIDENTS  102  FIGURE A V . 15 PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR LEFT TURN ACCIDENTS  103  FIGURE A V . 1 6 PREDICTED ACCIDENTS VS. A V E R A G E S Q U A R E D RESIDUALS AND ESTIMATED VARIANCE FOR LEFT TURN ACCIDENTS  103  FIGURE A V . 17 PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR RIGHT TURN ACCIDENTS  104  FIGURE A V . 1 8 PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE FOR RIGHT TURN ACCIDENTS  104  viii  M i c r o s c o p i c A c c i d e n t Prediction Models for Signalise;! Intersection*  LIST OF FIGURES FIGURE 2.1  APL  16  FIGURE 3.1  TIME OF ACCIDENTS  24  FIGURE 3.2  1994  ACCIDENTS  25  FIGURE 3.3  1995  ACCIDENTS  25  FIGURE 3.4  1996  ACCIDENTS  26  FIGURE 3.5  T O T A L ACCIDENTS  26  FIGURE 3.6  3 YEARS ACCIDENTS PER MONTH  27  FIGURE 3.7  CONTRIBUTING CIRCUMSTANCES  28  FIGURE 3.8  T Y P E OF ACCIDENT  29  FIGURE 3.9  COMBINED T Y P E S  30  IDENTIFICATION PROCESS  FIGURE 3.10  COMBINED T Y P E S P E R C E N T A G E S  30  FIGURE 3.11  ACCIDENT SEVERITY  31  FIGURE 3.12  ACCIDENT SEVERITY P E R C E N T A G E S  31  FIGURE 3.13  LIGHT CONDITION  32  FIGURE 3.14  ROAD CONDITION  32  FIGURE 3.15  W E A T H E R CONDITION  33  FIGURE 6.1  PREDICTED vs.  EB REFINED NUMBER OF ACCIDENTS  51  FIGURE 6.2  APL  FIGURE 6.3  CRITICAL C U R V E FOR AT1  59  FIGURE 6.4  CRITICAL C U R V E FOR AT5  60  FIGURE 6.5  CRITICAL C U R V E FOR AT7  61  FIGURE 6.6  CRITICAL C U R V E FOR AT13  62  FIGURE 6.7  CRITICAL C U R V E FOR L E F T TURN ACCIDENTS  63  FIGURE 6.8  CRITICAL C U R V E FOR RIGHT TURN ACCIDENTS  64  FIGURE 6.9  CRITICAL C U R V E FOR TOTAL ACCIDENTS  65  54  IDENTIFICATION PROCEDURE  FIGURE 6.10  CRITICAL C U R V E FOR S E V E R E ACCIDENTS  66  FIGURE 6.11  CRITICAL C U R V E FOR PDO  67  FIGURE 6.12  CRITICAL C U R V E S FOR VARIOUS K VALUES  ACCIDENTS  FIGURE  AIII.1  C O O K ' S DISTANCES BEFORE REMOVALS  FIGURE  AMI.2  C O O K ' S DISTANCES AFTER  FIGURE  All.3 C O O K ' S  FIGURE  AIII.4  FIGURE AV.1  2  WARRANTED AND  70  89 2  UNWARRANTED REMOVALS  DISTANCES BEFORE REMOVALS  C O O K ' S DISTANCES AFTER PREDICTED ACCIDENTS vs.  4  UNWARRANTED REMOVALS  PEARSON RESIDUALS FOR A T 1  89 90 90 96  (VHmwopte Accident P r e d i c t i o n M o d e l s f o r Signalised i n t e r s c M k o  FIGURE A V . 2  PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE  FORAT 1  96  FIGURE A V . 3 PREDICTED ACCIDENTS vs. FIGURE AV.4  PEARSON RESIDUALS FOR A T 5  97  PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE  FOR A T 5  97  FIGURE A V . 5 PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR A T 7  98  FIGURE A V . 6  PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE  FOR A T 7  98  FIGURE A V . 7 PREDICTED ACCIDENTS vs. FIGURE A V . 8  PEARSON RESIDUALS F O R A T 13  PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE  F O R A T 13  99  FIGURE A V . 9 PREDICTED ACCIDENTS vs. FIGURE A V . 1 0  99  PEARSON RESIDUALS FOR TOTAL ACCIDENTS  100  PREDICTED ACCIDENTS vs. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE  FOR TOTAL ACCIDENTS  100  FIGURE AV.11  PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR S E V E R E ACCIDENTS  101  FIGURE AV.12  PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE  FOR S E V E R E ACCIDENTS  101  FIGURE A V . 1 3  PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR P D O ACCIDENTS  102  FIGURE AV.14  PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE  FOR P D O ACCIDENTS  102  FIGURE A V . 15 PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR LEFT TURN ACCIDENTS  103  FIGURE A V . 1 6  PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE  FOR LEFT TURN ACCIDENTS  103  FIGURE A V . 17 PREDICTED ACCIDENTS VS. PEARSON RESIDUALS FOR RIGHT TURN ACCIDENTS  104  FIGURE A V . 1 8  PREDICTED ACCIDENTS VS. A V E R A G E SQUARED RESIDUALS AND ESTIMATED VARIANCE  FOR RIGHT TURN ACCIDENTS  104  X  i V i k r i w c o p k AU-M&PA  P r e d i c t i o H M o d e l * 1'or S i g n a l i s e d I n i e r s e e r i o n s  ACKNOWLEDGEMENTS My sincere thanks to Dr. Tarek Sayed, Assistant Professor, Department of Civil Engineering, University of British Columbia for the time he spent teaching me the theory behind traffic safety whose guidance and assistance were fundamental in the completion of this research.  And to Dr. Frank Navin,  Professor, Department of Civil Engineering, University of British Columbia for all the valuable insights I learnt from him about traffic flow and transportation planning. I would also like to thank The Insurance Corporation of British Columbia (ICBC) for providing the financial support for this thesis. I would also like to thank my parents, Dr. Mario Quintero and Guadalupe Toscano, and the rest of my family for their unwavering encouragement and support through the duration of this program. "De ustedes he aprendido lo que realmente importa"  xi  M i c r o s c o p i c Accident P r e d i c t i o n M o d e l s f o r Signalised i n t e r s e c t i o n s  1. INTRODUCTION 1.1 Background The literature reviewed showed that several researchers have dealt with the development of accident prediction models for isolated intersections (signalised and unsignalised) in a macroscopic fashion. The accident frequency has been related to the product of the average daily traffic on the major and minor roads. In this study, microscopic models, which relate the accident frequency to the traffic flows to which the colliding vehicles belong, will be developed. This should enhance the use of the models for safety planning purposes. There are two approaches for dealing with traffic safety problems: the reactive approach and the proactive approach. The reactive approach, or retrofit approach, consists of making the necessary improvements to existing hazardous sites in order to reduce accident frequency and severity at these sites. The proactive approach, on the other hand, is an accident prevention approach that tries to prevent unsafe conditions from occurring in the first place. However, one obstacle associated with the delivery of proactive road safety measures is the lack of the necessary tools to evaluate road safety in a proactive manner. For road safety decisions to be made early in the planning or design stage, it is important to understand the impact of an action on safety performance. t h e development of microscopic safety prediction models, which help explain the relationship between accident occurrence and the geometric and traffic parameters, is therefore invaluable for the success of the proactive safety approach. These models can be used by transportation planners to assess the impact of various transportation policies on traffic safety.  i  Microscopic Accident Prediction Models for Signalised Intersection!!  1.2 Thesis Objectives The objective of this thesis is to develop microscopic safety prediction models for urban signalised intersections in British Columbia. The models will be capable of estimating the frequency of the total accidents and specific accident types as a function of the detailed traffic flows at the intersection, including turning movements. The models are developed using the Generalised Linear Regression Modelling (GLIM) approach. The GLIM approach addresses and overcomes the shortcomings of the conventional linear regression approach in modelling the occurrence of traffic accidents (as will be explained later). The data on accident frequencies and traffic volumes were obtained from the city of Vancouver. Several models that have different applications in the field of traffic safety are developed in this study for the 4-leg signalised intersections of the City of Vancouver.  Microscopic models for different accident types are developed in  conjunction with macroscopic models for Total, Severe and Property Damage Only accidents. Several model applications are discussed.  2  M i c r o s c o p i c A c c i d e n t P r e d i c t i o n M o d e l s Cor S i g n a l i s e d I n t e r s e c t i o n s  2. PREVIOUS WORK  2.1 Introduction A literature review of the following areas was conducted: common practices used to assess safety at signalised intersections, the identification of Accident Prone Locations (APLs), and Accident Prediction Models (APMs) and their applications. It was found that the relationship between traffic accidents and traffic volume has been the subject of numerous studies. Most of the early studies used the conventional linear regression approach to develop models relating accidents to traffic volumes. However, several researchers have demonstrated the inappropriateness of conventional linear regression for modelling discrete and non-negative events such as traffic accidents. In this section, a brief outline of the major developments in accident prediction models will be presented.  2.2 Conventional Linear Regression Models (CLRM) Conventional Linear Regression Models (CLRM) were used by (Zegeer et al, 1986; Miaou and Lum, 1993) to described the empirical relationship between accidents, traffic, and road geometric design. The CLRM can be defined as follows: m  Y =a  +Y j i a  J  Q  x  J  .  +£  /=1,2,...,n  (2.1)  Where, Y  = estimated or dependent variable  3  ao, B\ = estimated coefficients, obtained by the least squares or the maximum likelihood methods xy  = independent variables  si  = estimated error, assumed to be normally distributed  The shortcomings associated with this approach have been documented in the literature by several researchers. Jovanis and Chang (1986) pointed out three shortcomings. First, the relationship between the mean and the variance of accident frequencies does not remain constant as a normal distribution assumes it should, the variance actually increases as the volume of traffic does. Second, conventional linear regression models can result in estimating negative accident frequencies for a data set that contains low accident frequencies. Third, the error distribution non-normality. Similar conclusions were reached by Miaou and Lum (1993). In addition, Miaou and Lum (1993) found that using a Poisson distribution error structure produced models with a better fit when compared with CLRM.  2.3 Generalised Linear Regression Modelling (GLIM) Generalized Linear Regression Modelling (GLIM) has the advantage of overcoming the limitations associated with conventional linear regression in modelling traffic accident occurrence. GLIM computer packages can be used for modelling data that follow a wide range of probability distributions such as the Normal, Poisson, binomial, negative binomial, gamma, and many others. These computer packages also allow the flexibility of using several non-linear model forms that can be converted into linear forms through the use of several built-in link functions.  4  M i c r o s c o p i c A c c i d e n t P r e d i c t i o n M o d e l s far Sinttaiiscnf Inlet-sections  The GLIM approach used herein is based on the work by work by Kulmala (1995) and Hauer et al (1988). It is assumed that Y is a random variable that describes the number of accidents at an intersection in a specific period of time, and y is the observation of this variable during that period of time. The mean of V is A which can also be regarded as a random variable. Then for A=A, Y is Poisson distributed with parameter A: P(Y = y\K = A) = i i f _ ; £ ( y | A = A);Var(Y\A = A) = A  (2.2)  Since each site has its own regional characteristics with a unique mean accident frequency A, Hauer et al, (1988) suggested that, for an imaginary group of sites with similar characteristics, A follows a gamma distribution described by the parameters *rand K/JU,  where K is the shape parameter of the distributions,  that is:  /  .  W  -  ^  l  ^  l  (2.3,  With a mean and variance of: E(h) = ju;Var(A) = —  (2.4)  K Kulmala (1995) showed that the point probability function of V based on equations 2.3 and 2.4 is given by the negative binomial distribution:  K  P(Y = y) =  n  K  +  y  )  (  K  ^ [ " )  K+Uj  (2.5)  K  With an expected value and variance of:  5  Mienwcopie .Accident Prediction M o d e l s 1'or Signalised intersections  E(Y) = t;Var(Y) = ti + ?r  (2.6)  K  It can be seen from equation 2.6 that the variance of the observed number of accidents is generally larger than its expected value, and it is composed of a first term (ju) from the variation of the number of accidents and by a second term ( ///K) from the variance of the predicted number of accidents. The only exception is when K goes to infinity, in which the distribution of A is concentrated at a point and the negative binomial distribution becomes identical to the Poisson distribution (Kulmala, 1995). When modelling traffic accident occurrence in the GLIM approach, as in the procedure described above, a Poisson or negative binomial error structure is assumed. The Poisson error structure offers the advantage of simplicity in the calculations because the mean and the variance are equal. Nevertheless, this advantage is also a limitation as it has been shown by shown by Kulmala and Roine (1988) and Kulmala (1995) when they demonstrated that most accident data are likely to be overdispersed. This means that the variance is greater than the mean (equation 2.6) which indicates that the negative binomial distribution is usually the more realistic assumption for the error structure. Three possible sources of overdispersion inherent to accident data were identified by Miaou and Lum (1993). The first is related to not including all explanatory variables when developing an APM. No model can include all of them because traffic accidents are the product of a complicated interaction between series of variables that sometimes are not available on the data set used to develop the APM. The second relates to the uncertainty involved in the vehicle exposure data and traffic variables that derive from errors during the data collection stage. And the third is due to using accident data that comes from a non-homogeneous roadway environment due to weather, light, road, traffic conditions, etc.  6  M i c r o s c o p i c Accident' P r e d i c t i o n M o d e l s for S i g n a l i s e d I n t e r s e c t i o n s  Bonneson and McCoy (1993) proposed a two step procedure to determine which error structure was the most appropriate for the data. First, a Poisson distribution error structure is used to determine the model coefficients (e.g. cc , Pi, P2)- After this, a dispersion parameter ad defined as in equation 2.7 0  is calculated.  ^ 7)  Pearsonx n-p  2  Where, n  = number of observations  p  = number of model parameters = Pearson test value defined as follow:  Pearson x  2  Pearsonx  2 =  V ^ " ^ j f ,=i Var(y )  (2.8)  :  Where, y,  = observed number of accidents on the intersection /  E ( A/)  = predicted number of accidents on the intersection /'  Var (y,)  = variance of the observed number of accidents (as defined in  equations 2.2 and 2.6) for the Poisson and negative binomial respectively If ad is greater than 1.0, then the data has greater dispersion than is explained by the Poisson distribution. Thus, further analysis with the negative binomial error structure is required. If ad is close to 1.0, then it can be said that the data approximately fits the Poisson error structure distribution. By using this method the model can be tested with an assumed Poisson distribution first, which requires less effort than the negative binomial distribution.  7  Microscopic Accident  Prediction  M o d e l s i'or S i g n a l i s e d I n t e r s e c t i o n s  The estimation of the shape parameter K when using the negative binomial error structure was studied by Kulmala (1995) who proposed an iterative approach employing the method of moments. Hauer et al (1988) used an iterative process in which an assumed value for *:was used to estimate the vector of coefficients with GLIM (e.g. a , Pi, P2), and after that the residuals were 0  calculated.  These residuals served as input into the program to obtain the  maximum likelihood estimate of K. The new K was then fed back into the program to obtain a new set of coefficients until closure was reached. The GLIM software package used for this study (version 4.0) includes a macro library in which the parameter A:can be calculated with three methods: the maximum likelihood, the mean deviance estimate, and the mean % estimate (NAG, 1996). 2  The method of maximum likelihood is used in this study.  2.4  Microscopic  Accident  Prediction  Models  (APMs) Detailed models that include several accident classifications, different impact types, or time periods have been studied previously. Lau et al (1989) proposed a method to estimate intersection safety by accident severity (Fatal, Injury, and PDO).  Poch and Mannering (1996), and Al-Turk and Moussavi  (1996) modelled the annual accident frequency for all accidents and for specific types (rear-end, angle, etc.). Hauer et al (1988) categorised accidents by the movement of the vehicle(s) before the collision and used the traffic flows to which the colliding vehicles belong to model specific accident types. For each recognised pattern, an accident prediction model was developed to estimate the expected number of accidents and the variance using the relevant traffic flows (through, left-turning or right-turning volume(s)).  8  2.4.1 Model Evolution Thorpe (1963), Smith (1970), and Worsey (1985) suggested that the total number of accidents at an intersection is proportional to the sum of all flows entering the intersection.  The advantage of this approach is its simplicity;  however, it fails in linking cause and effect of the accidents. Breuning and Bone (1959), Surti (1965), and Hakkert and Mahalel (1978) recommended relating accidents to the product of the entering flows. Webb (1955) and McDonald (1966) found in empirical research that accidents are related to the product flows with each flow raised to a power of less than one. Hauer et al, (1988), Nguyen and  (1997) developed  microscopic  models for accidents at signalised  intersections using the model of the product of flows raised to a power.  2.4.2 Model Form Hauer et al (1988) studied the general model intended for this project. This model relates accidents to the product of the traffic flows entering the intersection and it can be written as follow:  E{m} =  aF F e /h  Pl  0  (2.9)  Where E{m}  = predicted accident frequency  Fi  = major road traffic volume  F  = minor road traffic volume  2  a o , P i , p2, bj  = model coefficients  Xi  = any of the additional explanatory variables  9  Detailed APMs were also developed by (Bonneson and McCoy, 1993; Belanger, 1994) and had the following forms: (2.10)  E{m} = a F > fi  0  (2.11)  E{m} = a F F Pi  p2  0  Where, E {m}  = estimated parameter  ao, P i , P2  = estimated coefficients, obtained by Generalised Linear  Interactive Modelling Software F, Fi, F  2  = specific traffic flows which define the specific traffic pattern  In these equations the coefficients P i and P2 represent the changes of traffic flows and a represents the changes of any other factors. As it can be 0  seen, equation 2.10 is a special case of 2.11 and applies for all accident patterns that occur within the same traffic flow. As reported by (Persaud and Dzbik, 1993), these equations agree with the logic "no traffic flows, no accidents" and allow using non-linear relationships between accidents and traffic flows.  2.5 Outliers Outliers are observations from the data that diverge or are very distinct from the rest of the data. They can occur due to irregularities or inaccuracies incurred during the data gathering process or in the case when the data genuinely diverge from the rest of the observations. The leverage statistics, as proposed by Kulmala (1995), can be used to decide whether the observation should be removed or not. The leverage can be  10  M i e r w s c o n t e A c c i d e n t P r e d i c t i o n M o d e l s t o r S i g n a l i s e d (intersections  defined as the measure of how far the x-value of the point is away from the average of the rest of the x-values (NAG, 1994). NAG (1994) showed that the leverage by itself is not a good measure to warrant the removal of an observation. NAG (1994) also showed that a measure that can help to determine whether the parameter estimates are being affected by the specific observation is the Cook's distance. This distance is the measure of how the observations influence the model. Therefore, an observation which exhibit a high Cook's distance compared to the rest of the observations would have a stronger influence on the model than an observation that is clustered with the rest. The Cook's distance can be calculated as follow:  h.(r ') c,= ' ' />(!-*,) ps  2  (2.12)  A  Where, c  = Cook's distance  t  h  t  = Leverage  r '  = Standardised residuals  p  = Number of parameters  PS  v  No clear evidence of a procedure to determine which observations should be considered as having a high Cook's distance was found in the literature. NAG (1994) suggested sorting the data according to the Cook's distance and in an iterative process remove the points that exhibit the highest values.  This  procedure should be accompanied by the assessment of the change in the scaled deviance.  Maycock and Hall (1984) found that change in the scaled  deviance from a model that includes the complete data set (with dofi) to a model in which an observation has been removed (dof2), is x parameters dof dof . r  2  2  distributed with  By doing this the removal of an observation would be u  '  .  '  InlersM'rksns  warranted if it exhibits a higher Cook's distance than the % value for the desired 2  level of confidence.  2.6 Testing the Models Significance The Scaled Deviance (SD) and the Pearson x are usually utilised to 2  assess the significance of the GLIM models. The SD is defined as the likelihood test ratios measuring the difference between the log likelihood of the studied model, and the saturated model (Kulmala, 1995):  SD = 2 log f(y, y) - 2 log f(E(A), y)  (2.13)  Where, = The natural logarithm for the probability density  21og/(£(A),>>)  function For a Poisson model, (McCullagh and Nelder, 1993) showed that the SD is defined by:  (2.14)  And for a Negative Binomial distribution is defined by:  ( +K)\n yi  i=\  V  (2.15)  £(A/)/  The SD is asymptotically x distributed with n-p-1 degrees of freedom. 2  12  Another method to assess the statistical significance of the GLIM models is to use the Pearson % as in equation 2.8. In addition, several graphical 2  methods can be used. They will be reviewed in Section 4.3.  2.7 The Empirical Bayes Refinement (EB) There are two types of clues to the safety of a location: its traffic and road geometric design characteristics, and its historical accident data (Hauer, 1992, Briide and Larsson, 1988). The Empirical Bayes (EB) approach makes use of both kinds of clues. The EB approach is used to refine the estimate of the expected number of accidents at a location by combining the observed number of accidents at the location with the predicted number of accidents obtained from the GLIM model to yield more accurate, location-specific safety estimate. The EB estimated number of accidents for any intersection can be calculated using (Hauer et al. ,1992): EB  safely  = ccE(A) + (\-a)count  (2.16)  Where  a -  1+  Var(E(A))  (2.17)  E(A)  and, count  = observed number of accidents  13  M i e t w o n i c A c c i d e n t P r e d i c t i o n M o d e l s for S i g n a l i s e d interseeiions  E ( A)  = predicted number of accidents as estimated by the GLIM  model  Var (E ( A)) = variance of the GLIM estimate  Since Var(E(A)) =  E  ^  , then equation 2.16 can be rearranged to yield:  K  E(A) ^  r  safely estimate  K  (K +count)  (2.18)  + E(A)  The variance of the EB estimate can also be calculated by:  E(A)  r  Var(EB safety ) = safelv  estimate  K  K  V  + E(A);  {K +count)  (2.19)  As shown, the refinement estimates for any entity can be known by using the EB method if the predicted accident frequency and its variance from a reference population that shares similar physical characteristics with the entity and the accident history of the entity are known. These refinement estimates, that combine the information from both sources, should be used to decide whether the entity is unsafe. In addition, they can be used to assess the safety effects of countermeasures applied to the entity.  2.8 Accident Prone Locations (APLs) Accident-prone locations (APLs) are defined as locations that exhibit a significant number of accidents compared to a specific norm. Because of the randomness inherent in accident occurrence, statistical techniques that account for this randomness should be used when identifying APLs. Higle and Witkowski  14  (1988) and Belanger (1994) proposed the use of the EB refinement method to account for this randomness as follow: Using the appropriate GLIM model estimate, determine the predicted number of accidents and its variance for the intersection. This would follow a gamma distribution (the prior distribution that represents the distribution of the expected accident frequency for a population of intersections with similar characteristics to the intersection analysed), that is defined by the parameters aj and Pi, where:  = 1^  ^V^  P  B  N  OC =B,-E(K)  D  X  (2.20)  = K  Determine the appropriate point of comparison based on mean and variance values obtained previously. Using the 50 percentile (P ) is a practice th  50  commonly accepted, another possibility is to use the mean E ( A).  P50 is  calculated such that:  KKlE(k))  -A  -e  d  X  =  Q  5  Calculate the EB safety estimate and its variance using equations 2.18 and 2.19. This is also a gamma distribution, called posterior distribution, and defined by the parameters a and p , where: 2  EB 2  Var(EB)  =  _^_  + 1  2  a  n  d  a  - p  EB = K + count  (2.22)  E(A)  Then, the probability density function of the posterior distribution is:  15  M i c r o s c o p i c A c c i d e n t P r e d i c t i o n M o d e l s for S i g n a l i s e d i n t e r s e c t i o n s  (KIE(A)  +  \ y  K  +  c  o  m  " ) .  ^(K ° '-v +c un  .  g-(tf/£(A)+i)A  (2.23)  r(/c + count)  Identify the intersection as A P L s if there is significant probability that the intersection's safety estimate exceeds the P o value. 5  Thus, the location would  be identified as A P L s if:  ' 50  1-f.  T(K + count)  (2.24)  dX > S  Where 8 represents the confidence level desired (usually 0.95)  Prior and Posterior Distributions  • Prior ' Posterior  Probability of safety estimate less than Pso  Figure 2.1 A P L identification process  16  M i c r o s c o p i c Accident P r e d i c t i o n M o d e l s for Signalised intersections  2.9 APMs for 4-leg signalised intersections Microscopic APMs were developed by Hauer et al (1988) using data from 145 4-leg, fixed time, signalized intersections from Metropolitan Toronto. These intersections carried two-way traffic and had no turn restrictions. The majority of the intersections were on straight, level sites with a speed limit of 60 km/hr. Detailed flows for the AM peak, PM peak, and off-peak were collected manually (for weekday conditions). Only collisions involving two vehicles were analysed for the period between 1982 and 1984. APMs for rear end, intersection 90°, and opposing left turn were reported as significant models, and the rest of the accident patterns were provided but regarded as unreliable. The APMs that correspond to patterns studied in this project are summarised in Table 2.1. Similar APMs were developed by Nguyen (1997).  A total of 254  signalised intersections maintained by the Central and Southwest regions of Ontario were used for this purpose. From these intersections, 197 were rural, 33 were semi-urban, and 24 were located in urban areas. Directional traffic flows for AM peak, PM peak, AADT and accident counts from 1988 to 1993 were used to develop the models. 25 Patterns were analysed, however, just 11 patterns had emerged as statistically significant models. Models for each of the different areas were developed for PDO and Severe accidents. The significant PDO and Severe APMs that correspond to patterns analysed in this project are presented in Tables 2.2 and 2.3.  17  M k r o s e o p i e A c d d e s i i P r e d i c t i o n M o d e l s for Signalise*! i o f e r s e e i i o n s  Pattern  JU  E{m}= 0.2052X10" *F E{m}= 8.6129X10" *F 9  f i j F1  6  6  0  5.51  3662  2  E{m}= 1.7741X10- *F 9  (rn  E{m}= 0.4846X10- *F 6  Fl?"  1U21 1  *F -  5467  1,2  *F -  4479  1,2  0  2  a2769 1  0  2  1,2  E{m}= 2.6792X10" *F ' 6  0 2476  2  H < f = F ^ E{m}=  0.2113X10- *F °^*F -'' 6  0  1  E{m}= 0.0418X10" *F 6  0  *  1,2  1 0 6 8 2 2  E{m}= 8.1296X10" *F -  "  E{m}= 0.1014X10" *F 4.59 and 1.97  and  6  051  2  4634  2  1,2 2.10  Table 2.1 APMs for 4-leg signalised intersections (Hauer et al, 1988) Where, *  = Models studied in detail, the rest of the models were regarded as  "unreliable" because of the limited data F  = Sum of flows  Fi  = Major flow (for the third and fourth models), straight flow (for  models that have a turning flow) F  2  = Minor flow or turning flow (refer to Fi explanation for)  All flows in vehicles per hour Note: As observed in Table 2.1, some of the models suggest the use of a K range instead of giving a specific value. These models were regarded as unreliable by the authors.  18  Micrftsconic A c c i d e n t Prediction M o d e l s f o r Signalised Intersections  Pattern  >1AW5 E{m}= 29.9X10" *F 6  Fl^'i  and  1 0 7 7 1  E { m } = 16929X10" *F 6  0  K  E{m}= 41.8X10' *F 6  6  676  1  *F 0  0.43  206  2  344  1.17  0 0 8 3  1.21  E { m } = 269.3XIO- *F °- *F 6  340  0  1  2  E{m}= 237.4X10" *F 6  a 5 6 7 1  E{m}= 2331.4X10" *F °6  *F 369  1  57  0  1  2  *F  E { m } = 38.7X10- *F °- *F 6  3.15 and 1.52 0.99  2 5 5  2  E{m}= 47.3X10" *F °-  li <^ F2  0 9 2 5 1  1.55  0032 2  2.40  604  2  Table 2.2 PDO APMs for4-leg signalised intersections (Nguyen, 1997) APMs SEVERE  Pattern c=>"c=>  E{m}= 3.38X10' *F 6  and  1 2 4 9 2  111 B  E{m}= 3549.8X10" *F  2  iTVn  E{m}= 2.6X10- *F °-  *F -  6  6  E{m}= 57.4X10" *F 6  576  0  0  E { m } = 70.3X10" *F °6  3 4 0  567  1  206  0.43  ^,  0 3 4 4  1.17  *F  0 8 3  1.21  0 2  E{m}= l S l Q . e X l O - ^ F ^ ^ ^ F z ' 0  E{m}= H.eXlO- ^! 0  6 9 9  4.05 and 0.77 0.089  2  E{m}= S l . l X l O - ^ ! -  6  a 7 8 8 2  0423  1  6  K  ^,  0 3 2  0 5 4 2  1.55 2.05  Table 2.3 SEVERE APMs for 4-leg signalised intersections (Nguyen, 1997) Where, F, Fi, and F are as described for the first set of models 2  Traffic volumes in AADT  19  Microscopic A c c i d e n t P r e d i c t i o n Models for S i g n a l i s e d I n t e r s e c t i o n s  Both studies differentiated rear end accidents that occur before the intersections and in the intersection. These two models are shown in the same cell (first row cell), their K'S are reported in the next cell respectively. Similar models for the Greater Vancouver Regional District were developed by Feng (1997). 72 urban intersections form the City of Vancouver and 67 from the City of Richmond were used for this purpose.  The APMs  developed in this study intended to relate accidents to their traffic and road variables.  Models for specific accident types and aggregate models were  developed. They included severity types, different time during the day, and peak periods. The models that are relevant for this project are summarised in Table 2.4.  APMs  Pattern  K 26.43  E{m}= 0.0627*F '  1 2360  TOTAL  E{m}= 2.1813*F  1  SEVERE  E{m}= 0.9049*F  1  PDO  E{m}= 1 . 3 0 1 2 * F  03286  4418  8.4  *F -  4490  18.45  a4412  11.27  a4950  3.42  0  2  a2836  LEFT TURN E{m}= 0 . 5 5 7 2 * F  *F 0  2  a3409 1  a2999 1  *F  2  *F  2  Table 2.4 APMs for signalised intersections (Feng, 1997)  Where, F  = Sum of all entering flows  Fi = Major road volume F  2  = Minor road volume  All volumes are AADT in thousands  20  M k r o s f o p i c A c t - i f l c M t P r e d i c t i o n M o d e l s for S i g n a l i s e d I n t e r s e c t i o n s  3. DATA DESCRIPTION 3.1 Introduction The accident data used for this project consists of 8466 accidents reported in a set of 170 four-legged signalised intersections in the City of Vancouver from 1994 to 1996.  The MV104 accident reporting form, British  Columbia's accident police report, provided the information needed to group the accidents into the 17 accident types according to vehicle's movements before the crash (16 accident types plus the type "other" explained by police comments). The MV104 form categorises accidents into the 17 types shown in Table 3.1. The volume data was obtained from the City of Vancouver.  3.2 Data Analysis After summarising the accident information provided by the MV 104, several tables were developed.  They are included in the Data Analysis  Appendix. The time, date and the accident contributing factors are summarised in Tables AIM, All.2 and All.3. As shown, more accidents occurred during the daytime period, reaching a maximum between 16:00 and 16:59 and a minimum between 4:00 and 4:59. Accidents seem to occur less during the summer and to be fairly constant over the rest of the year. The contributing circumstances were not reported for all accidents. More than two thirds (67.77%) of the accidents did not have a contributing circumstance reported. The most frequent circumstance was failing to yield.  21  M i c r o s c o p i c A c c i d e n t P r e d i c t i o n M o d e l s f o r S i g n a l i s e d intersections  Accident Type  Description  AT 1  Rear end  AT 2  Head on  AT 3  Side swipe  AT 4  Backing  AT 5  Intersection 90°  AT 6  Overtaking  AT 7  Right Turn  AT 8  Right Turn  AT 9  Right Turn  A T 10  Right Turn  A T 11  Left Turn  A T 12  Left Turn  A T 13  Left Turn •  A T 14  Pattern  -  Off road right  Off road left  One w a y street  t  Other " e x p l a i n e d by police c o m m e n t s "  t  N. A.  Table 3.1 The MV 104 Form: Accident types, traffic patterns  22  M i c r o s c o p i c A c c i d e n t P r e d i c t i o n iVfotiels for Signalised intersecilosrs  Accident types are summarised in Table AII.4 where it can be observed that Rear end (Accident Type 1), Left turn (Accident Type 13), and Intersection 90° (Accident Type 5) were the most common types; these three types alone represent 77.4% of the all accidents. This finding agrees with previous research (Hauer et al, 1988) where it was pointed out that these three accident types accounted for 83% of the accidents in their study.  Light, road, and weather  conditions are shown in Tables All.5, All.6 and All.7. The most frequent light, road, and weather conditions were daytime, clear, and dry respectively. Tables All.8, All.9 and All. 10 contain the volume information. The volume information was summarised according to movement type (e.g. Northbound Through, Northbound Right turn, Northbound Left Turn, etc.), approach, and by intersection. Some useful statistics were calculated when possible. The maximum and minimum count, average, standard deviation, median, and variance were calculated for all tables. Count percentages were calculated for Tables 6.3 through 6.7. Unreported information was a problem and is noted in Table 6.3 and 6.6. Table 6.3, Contributing Circumstances, indicates that just 32.23% of the accidents analysed for this report had a contributing circumstance assigned by the police officer (more than two thirds of the accidents did not have a reported contributing circumstance). Table 6.6, Road Conditions, indicates 25 unreported accidents (0.295% of the accidents) which did not have a road associated contributing circumstance. In addition, several graphs were used to summarise the data. Figure 3.1 shows the accidents that occurred at each hour during the day. As mentioned before, the period from 16:00 to 16:59 presented the highest count and the period from 4:00 to 4:59 the lowest. Daytime hours have higher accident counts, as would be expected.  23  V i i f r o s c o j i i c A c c i d e n t Prediction M o d e l s f o r S i g n a l i s e d i n t e r s c c t t o n s  Figure 3.1 Time of Accidents  HOUR OF ACCIDENTS 800  0 £ 600 £ 9 400  § o  1  ^ 200 0 0  1  2  3  4  5  6  7  9  iin  10 11 12 13 14 15 16 17 18 19 20 21 22 23  HOUR  Figures 3.2 to 3.4 show the accident count for each month from 1994 to 1996. The average accident counts per month for the 3 years studied was 235 accidents/month.  24  YiieroM'isjtic Accident Prediction Models for Signalised Intersections  Figure 3.2 1994 Accidents  1994 ACCIDENTS 400 £ £ 300  onLU LU m  9  o o <  2  0  0  100 0  JAN  FEB  MAR APR MAY  JUN  JUL  AUG  SEP  OCT NOV DEC  MONTH  Figure 3.3 1995 Accidents  1995 ACCIDENTS 400 U-  (J)  O hi 300  g Q 200 |  ^ 100 0  JAN  FEB  MAR  APR  MAY  JUN  JUL  AUG  SEP  OCT  NOV  DEC  MONTH  25  M i r r w c o i i k A c c i d e n t Prediction M o d e l s f o r Signalised Intersections  Figure 3.4 1996 Accidents  1996 ACCIDENTS 300 u_ (/) 250 ° Z 200 S 150 =  18  1 0 0  z <  50 0  JAN  FEB MAR APR MAY JUN JUL AUG SEP  OCT NOV DEC  MONTH  Figure 3.5 shows the accident count per year for the 1994 to 1996 period. Figure 3.6 compares the 3 years accident count simultaneously.  Figure 3.5 Total Accidents  3 YEARS ACCIDENTS 3200 £ CO 3000 g m 2800 | § 2600 I < 2400 2200  1995  YEAR  26  Microscopic A c c i d e n t P r e d i c t i o n M o d e l s f o r Signalised Intersections  Figure 3.6 3 years Accidents per Month  3 YEARS ACCIDENTS 350  JAN  FEB  MAR  APR  MAY  JUN  JUL  AUG  SEP  OCT  NOV  DEC  MONTH  The contributing circumstances are shown on Figure 3.7.  As stated  before, just 2729 out of the 8466 accidents had a reported contributing circumstance.  Failing to yield was the most frequently reported contributing  circumstance. Figure 3.8 shows the accidents by type according to the MV 104. Rear end (type 1), Head on (type 2), Intersection 90° (type 5) are the single types with the highest count.  27  aais O N O M M H3H1V3AA 2  aaivdrasiA  El  ascno ooi nvdauu.  c/)  DNI033dS  UJ  siuaaa avou  o z  aiOOV A3Hd  <  3ZISd3AO  I-  HSRLO NOUVOICBW an aynossNi  s  c  Nynidoudwi SSVd dOddl/MI  o  SS3N11I  sonua 11 NCO d l UN9I UOIddO UN9I  o  Nns-auvio diidv-gavno d33nSVTBd anoiivd aiiuivd s i s nivd dxaNi Aiya  iiVNI Alda  CQ  i H o n a H daa H o n x a a dsa  5 C  3»vaa dia  Niino j/io>iovia  O O  Q advsNnxova »  |  a/A/daioAv "lOHCOIV  o o  o o  o o  o o  o o  o o  o o  o o  o o  O C O S C D l O ^ - C O C v l ^ -  o  CO O OO  a3HU0 91 ±V Sl IV IV  O CN O CM CM  CO  Cl IV 31 IV  CD  CM  z LU  11 IV  Gi  CO CO  Q  o  01 ±v n• 61V  0 0  LO CD  o  LL  o  [  g ilV  co  LU Q. >-  LO LO CO  81V  9 IV —  1  S1V tlV  LO  I CIV 31V  M"  I IV  o o o  CO  o  o  LO CN  CM  o  o o  O  O  LO  o o o  o o  LO  o  Microscopic Accident Prediction Models for Signalised Intersections  Figures 3.9 and 3.10 include accident types that showed higher counts: Accident types 1, 2, 5, left turn (types 11, 12 and 13) and right turn (types 7, 8, 9, and 10) by count and by percentage respectively.  Figure 3.9 Combined Types  TYPES OF ACCIDENT 3000 2500 2000 1500 1000 500 0  2692  2385 1655 497  REAR END  HEAD ON  372 RIGHT ANGLE  LTs  RT's  Figure 3.10 Combined Types Percentages  TYPES OF ACCIDENTS  30  Microscopic Accident Prediction Models for Signalised Intersections  Figures 3.11 and 3.12 summarise accidents by severity, count, and percentage respectively. Figure 3.11 Accident Severity  ACCIDENTS BY SEVERITY 8000 5963 6000 4000  2493  2000 10 0 Injury  Fatal  P  D  O  Figure 3.12 Accident Severity Percentages  ACCIDENTS BY SEVERITY 0% • Fatal • Injury • PDO  31  Microscopic Accident Prediction Models for Signalised Intersections  Finally Figures 3.13, 3.14, and 3.15 show the different light, road, and weather conditions at which accidents occurred. Day (light), Dry (road surface), and Clear (weather) were the most frequently reported accident conditions.  Figure 3.13 Light Condition  LIGHT CONDITION  5132  7000 6000 5000 4000 3000  1707  2000 1000  27  629  95  Q  20  XZL  0  <  256  <  <  co  Q  Q  Z) Q  Q  3  Figure 3.14 Road Condition  ROADWAY S U R F A C E CONDITION 6000  5311  5000 4000  3035  3000 2000 1000  16  12  12  >-  LU  o  x  Q  Q D  55  0  LY.  CO  O  CO  z  D _]  hiu  CO 32  Microscopic Accident Prediction Models for Signalised Intersections  Figure 3.15 Weather Condition  WEATHER CONDITION 4 6 8 5  33  Microscopic Accident Prediction Models for Signalised Intersections  4. MODELS DEVELOPMENT 4.1 The Models Accident Prediction Models (APMs) were developed for all accident types as specified in the MV104 form excluding accident type 3,14,15,16, and 17 (See Table 4.2). In addition to the 12 accident types, 2 models that combined the turning movements and 3 macroscopic models were developed. The combined models are left turn accidents (types 11, 12, and 13 combined), and right turn accidents (types 7, 8, 9, and 10 combined). The macroscopic models developed correspond to the total number, severe (fatal plus injury), and PDO.  The  criterion of major and minor volumes was used to find the flows for the macroscopic models (i.e. the larger volume was regarded as the major volume and the smaller as the minor). The models and traffic patterns used are summarised in Tables 4.1 and 4.2. Equation 2.10 is used for accident types related to a single flow in which F denotes the sum of all volumes entering the intersection. Equation 2.11 is used for models that are described by two traffic volumes, where Fi is the larger volume. Head on accident (AT 2) traffic volumes are defined as a special case; Fi  is  the  larger  volume  between  the  combination  of  southbound  through/westbound through and northbound through/eastbound through and F  2  the smaller of the two volumes. By doing this all the possible head on colliding trajectories are covered.  Also, for Intersections 90° accidents (AT 5), the  treatment of traffic volumes is a special case. Southbound through (SBT) and Northbound through (NBT) volumes are added to form a combined volume, and Westbound through (WBT) and Eastbound through (EBT) volumes are also added to constitute the second volume. sorted as major and minor volumes.  These combined volumes are then  All models use major and minor road  volumes in 1000s AADT.  34  Microscopic Accident Prediction Models for Signalised Intersections  Accident TypePattern  F  F.  2  E {m}  ' Approaches  AT 1  Equation  F  =a P  AT 2  Major  Minor  E {m}  =a  AT 3  Major Road  Minor Road  E {m>  = a F/  AT 5  Major  Minor  F/  1  - ao P  =a  E {m}  . Approaches  AT 6  E {m}  Fi F/ 151  0  0  E {m}  L Approaches  AT 4  1  0  2  2  1  F^ F/ 1  0  =a P  2  1  0  i Through  Z Right Turn  E {m}  =a  0  F/ F/  AT 8  \ Through  Z Right Turn  E {m}  =a  0  F/ F/  AT 9  1 Through  Z Right Turn  E {m}  = ao  F/ F/ 1  2  AT 10  l Through  Z Right Turn  E {m}  =a  1  F^ F/  2  0  Al 11  l Through  £ Left Turn  E {m}  =a  F/  2  0  Through  z Left Turn  E {m}  =a  0  >. Through  Z Left Turn  E {m}  = a Fx"  <=>tf>  AT 7  AT 12  AT'14  ', ; V  AT 15 AT it) N. A  1  2  1  2  F/ F/  2  F/  2  1  1  0  I Approaches  N. A.  Z Approaches  N. A. N. A.  N. A.  N. A.  N. A.  * Major and minor between the combination of SB-WB and NB-EB  ** Major and minor between SBT+NBT and WBT-EBT  (refer to previous page for further explanation about these cases) Table 4.1 Accident types, defining patterns and flows, and equations used to develop the models (for accident description refer to table 3.1)  35  Microscopic Accident Prediction Models for Signalised Intersections  9  8 7 6 5 4  10  •mJ&'  12 12  NB+SB WB+EB SB+WB NB+EB  3  1+2+3+7+8+9 4+5+6+10+11+12 7+8+9+4+5+6 1+2+3+10+11+12 Fi  AT 1 AT 2 AT 3 AT 4 AT 5 AT 6 AT 7 AT 8 AT 9 AT 10 AT 11 AT 12 AT 13 RT LT TOTAL I SEVERE PDO  X of all Flows Highest of SB+WB and NB+EB Highest of NB+SB and WB+EB Z of all Flows Highest of 2+8 and 5+11 S of all Flows 2+5+8+11 2+5+8+11 2+5+8+11 2+5+8+11 2+5+8+11 2+5+8+11 2+5+8+11 2+5+8+11 2+5+8+11 Highest of NB+SB and WB+EB Highest of NB+SB and WB+EB Highest of NB+SB and WB+EB  Not used Smallest of SB+WB and NB+EB Smallest of NB+SB and WB+EB Not used Smallest of_2+8 and 5+11 Not used | 3+6+9+12 3+6+9+12 3+6+9+12 3+6+9+12 1+4+7+10 1+4+7+10 1+4+7+10 3+6+9+12 1+4+7+10 Smallest of NB+SB and WB+EB Smallest of NB+SB and WB+EB Smallest of NB+SB and WB+EB  Table 4.2 Traffic flows graphical explanation  36  Microscopic Accident Prediction Models for Signalised Intersections  4.2 Outlier Analysis The GLIM software used for this study has the capacity to calculate the values of leverage and Cook's distance. The procedure followed in this study to identify the outliers in a systematic manner involves the visual examination of the observed number of accidents, at any given intersection, versus the Cook's distance. The intersection that exhibits the highest the Cook's distance (c ) is t  identified and consequently removed. After the removal of the intersection, a new model is developed and the change in scaled deviance with respect to the previous model is assessed. This difference is compared to the % value for the 2  desired level of confidence with a dof (degrees of freedom) that corresponds to the number of intersections removed. The previous procedure was applied to all models in this study. After the analysis was performed two of the models had intersections which warranted removal from the original database.  Intersection (INT) 87 (refer to the  intersection list appendix Table AI.1) was removed for the AT 7 (left turn) model and INT 169 and INT 170 for the PDO model. A Sample analysis is provided in the Outlier Analysis Samples Appendix.  4.3 Testing the Models' Statistical Significance Several ways to test the statistical significance of the models can be used. For this project it was decided to use the Scale Deviance, the Pearson % and 2  the t-ratio test. The scale deviance (SD) should be approximately equal to the degrees of freedom (dof). The Pearson % obtained from the model should be 2  less than the x distribution value that corresponds to the degrees of freedom for 2  the model tested. The t-ratio test can be used to asses the significance of the model's coefficients, in this case the ratio of the estimated coefficient and its  37  Microscopic Accident Prediction Models for Signalised Intersections  standard error should be greater than the t-test value (1.96 for a 95% confidence). In addition, graphical methods can be used to test the models. Two methods were used for this project. The first is the plot of the Average Squared Residuals (ASR) versus the Predicted Accident Frequency (PAF). In this case the data points of a model with a good fit should be clustered around the variance function line for the NB distribution. The second is the plot of the Pearson Residuals (PR) versus predicted accident frequency. A model with a good fit should have the PR values clustered around the zero axis over the range of E (A) (Bonneson and McCoy, 1997). The Pearson Residuals are defined as follow (NAG, 1994): (4.1)  £ C A ^  JVar( ) yi  Where, E (A,-)  = predicted accident frequency  y,  = observed accidents  ^Var(y ) t  = standard deviation  A total of 17 models were developed (14 microscopic and 3 macroscopic) After applying the statistical tests used to asses the model's significance, 9 models were considered to be statistically significant. All models developed are presented in Table 4.2. The models shown in shaded rows are not statistically significant according to at least one of the tests used for this research, thus they may not provide reliable estimates of accidents.  38  MkTi>sfojiit-Aixiikiit Prediction Models  Atad^nt Type AI  . • - ,•  a  1.5210  >  0 0418  x  V  _ J ,  2 5971  x  MA  -8.23  0  Pi  0.1501 a  0.3177 S  AT 7  0.5462 a  0.6149 ="  0.0283  (f>  x  T  x RT  p  t o  0.3027  x  T  oCO  NOT SIGNIFICANT APMs  MICROSCOPIC AP. \  s  I1  COMBINED LEt T 7URU  RI  COMBINED RIGHT TURN  0.4423  x  T  x LT  0.1703  x  T  0.5468 x RT  2  —  Pi  2.136  ( 197 )  2.8/4  ( 197 )  159  192.1  2.92 (  167 )  2.94 184.8  193.4 0.95 (  167)  1.36 0.8822  AT *  0 X8S  AT 6  0.0003  ATS  "..  v  a  1.4790 x  0.3831 «  0.7334  ft  "  C" J I S ' J  T  LGS )  ( 298 )  if-s)  ( 198  182  .1.99 ( -4.06  0  .  )V  236  192.4  2.53 (  [i,  RT  2.26 :  -2.73  0  h  V  190  2.07  .j.-  '  167 )  ( 197 ) ,  0.28  AT  0.5642 . . .  9  fi  'Sb'l  T  -3.15  AT 10  (.' JP21  -0 :038  , - -2  .i,  2 2/  0.1275  ^)  x  T  .  U 2087 a ,< L T  fTERNS  x MA  rrj  ALL PATTERNS  x  MI  Pi P  0.4914 a  0.7330 0.7628  x MA  x  MI  0.4958  x MA  x  MI  -2.11 0.62 (  1.37 7.52 (  B,  7.63 (  p  7.24  2  0  Pi p  '  ••  '  176  180.2  i  167 1  Wl  \  2  136  179.5 5.064  ( 197 )  167 )  4.728  ( 197 )  165 )  4.857  ( 195 )  167 )  6.84 -0.91  0  0.3402 a  0.7152  267  •••  167 )  0.2:  0  2  ALL PATTERNS  Q  .. 1.4592  ( 197 )  1.7t  Pi  0.4387 a  0.6790  scvrRE  ) ..  (  0.2163  235  128.1  1.2:  ..  • OlAL  07  -0 01 (  ib  AI  lb/ )  1 522  2.20  :»j  <—  159  168.9  1.8i (  RT  AT11  1 MACROSCOPIC APMs SIGNIFICANT APMs  ( 197 )  166  167 )  2.33 -3.16  p!  HI  2.242  189.8  7*7 (  0.3959  MA  0 '38P -  —  ( 197 )  2.19  .. 0.3036  2.160  165  167 )  -2.06  0  ,. 0.4740  ( 196 )  192.9  7.89 (  .-.  TYPE:',  AT  -2.89  2  TYPe*  4.581  171  166 )  2.07  2  0  0.2532 u  0.8480  ( 197 )  161.7  2.46 (  P  cc  3.588  5.40  p,  < LT  167 )  -4.01  0  0.2441 a  0 9327  184  187.9  3.42 (  Pi  »-  167.7  ( 198 )  168 )  3.23  0  Pi P  MI  x  Pearson / ("/.Jest J,.-  K  5.560  179.7  15.36 (  2  0.  SD ( dof )  t-test  Model  Pattern (MV104)  1  .il  ft.,  141  181.2  150  -2.05 6.66 ( 4.66  Table 4.3 Accident Prediction Models for specific accident types and for aggregate types Shaded models are not statistically  significant  39  M i c r o s c o p i c A c c k t e m P r e d i c t i o n M o d e l s for S i g n a l i s e d i n t e r s e c t i o n s  5.SELECTION OF A P M TYPE 5.1 Introduction Previous research by Miaou (1996) mentioned four types of regression models which are widely used.  These types include the normal linear, the  lognormal, the Poisson, and the Negative Binomial regression models. Linear regression models are discarded for this type thesis because the linear relationships are inappropriate to describe accidents and traffic flows (Hauer et al, 1988; Persaud and Dzbik, 1993; and Miaou et al, 1996). Also, it has been shown that the lognormal regression model is inadequate to model accidentflow-roadway design relationships (Miaou, 1996). Therefore, the Poisson and NB regression models are going to be considered for further analysis.  5.2 Poisson Model The probability function of a Poisson model can be written as:  (5.1)  p\x\m, n  Where,  x  the number of accident counts at an intersection per n years  p\x\m,n  the probability that x accidents are expected to occur at an  intersection per n years mn  = the Poisson parameter, accident frequency expected to  occur at an intersection per n years  40  Microscopic A r c i o t i r t Pmlksiors M o d e l s f o r Si«swit«Hl isslerscciions  The Poisson model offers the advantage of capturing the two main properties of traffic accident counts, discrete and nonnegative, when modelling accident distribution at an intersection, m. However, for regression models the estimation of E{m} is of interest and not the accident frequency. As explained earlier, the estimation of E{m} is based on a group of intersections (reference population). It can be observed that because of the complexity of traffic accidents, and given that a high level of homogeneity has been achieved forming a reference population, the accident frequency still vary from intersection to intersection even when they are subject to the same traffic flows.  According to Hauer and Persaud (1988) when a Poisson  distribution is used to represent the accident frequency at each intersection and the distribution of the accident frequency around the E{m} is described by the Gamma probability distribution then: (5.2)  E{x}=E{m}  (5.3)  Var{x} = E{m}+ Var{m)  Where, E{x}  = mean accident counts of the reference population  Var{x}  = variance of the accident counts in a reference population  E{m}  = mean of the reference population  Var{m} = variance of the reference population It can be observed that in a reference population the E{x} and Var{x} are not equal, and that the Var{x} exceeds the E{x}. These equations have been accepted and used in previous research (Hauer et al, 1988; Persaud and Dzbik, 1993; and Belanger, 1994).  Thus, to properly model accident counts in a  reference population the previous equations have to be satisfied by the statistical model chosen.  41  The distribution of each accident frequency around the E{m} of the reference population has been described before as one of a family of "compound Poisson distribution" (Hauer et al, 1988). It can be implied form this description that the variance of accident frequencies around the E{m} is composed by the randomness of the accidents at each intersection and the differences among the accident frequencies in the reference population. The Poisson model is able to account for the randomness of the accidents but fails to account for the differences among the accident frequencies. In conclusion, it can be said that the use of Poisson models is inappropriate for regression models intended to predict the safety property of a reference population because it can not convey the fact that Var{x} > E{x}. However, it can be used as a platform to find a model that could be used for such a purpose.  5.3 Negative Binomial Model As shown by Hauer and Persaud (1988), the distribution of the accident frequencies in a reference population can be described by the Gamma probability distribution. Hauer et al, (1988), proved that if the accident counts at an intersection is Poisson distributed and if in a group of intersections the accident frequencies are Gamma distributed, then the distribution of accident counts in the group of intersections follows the NB distribution. The Gamma probability density function can be expressed as:  f E{m)  f{m) =  V - M ^  V Vctr{m}  (5.4)  For m>0 and 0 otherwise  42  Microscopic Accident Prediction Models for Signalised intersections  The Gamma probability density function is obviously appropriate to describe the distribution of accident frequencies around E{m} because it can take any positive values of accident frequencies. Also, as expressed in Equation 5.4, this distribution can be manipulated to fit a large number of shapes by stretching or skewing it as seen in Equation 5.6. As it was described before, p(x\m) can be calculated as:  p[x\m  m  (5.5)  The distribution of m's in the reference population is indicated by Equation 5.4; therefore:  (5.6)  p(x)= jp(x\m)df(m)  Forx=0,1,2,... and where, from mathematical manipulation from Equation 5.4  f E{m} V ' - M  df(m) =  V Var^n\J  \dm  (5.7)  r  Thus,  43  Mieroseonfc A c c i d e n t Prediction Models f o r Signalised Intersections  E{m] w  -m  e  m  Var{m)  ^  E{III  r g{»}  Var{m}  +1 |  2  f f  \  £{m}  (5.8)  \dm  2 \  E{m]  r  r  Var{m) '  Kor{n;  Var{m]  x  I, f o r t  JC!  x+  m  E{n. Var \m)  (5.9)  dm  Var{m]  T?L.A \ E{m}  V  E{,„f Var{m}  Var{m) 2  ' E{m)  \  x + Var{m]  1 x!  E{m}  Var{m) A V  Var{m)  +1  (5.10)  Var {m }  Or,  dw-{m}  ix) =  +  2  \ r +1  Var{m}\ Var{m}  Var{m) E{m\  E{m} ( E{m}2  1  Var{m]  ^  (5.11)  j  E{m}  2  + 1 •x\  Equation 5.11 is the Negative Binomial distribution. It can be shown by several methods that the mean and the variance of the NB models follow the forms sought in Equations 5.2 and 5.3. A comparison of the results obtained by models developed using the Poisson and NB distributions will be presented in the next section.  44  Miefwseoitie Accident Prediction  Mtidets  for S i g n a l i s e d Intersections  5.4 Accident Database Evidence As reviewed in the previous sections, the NB model should be used to develop safety equations for traffic accidents. Nevertheless, it is worth while to verify the theory using the real data for this research. To start the database inspection dispersion parameters, a d , can be used to decide which error structure fits the data the best.  If the dispersion parameter in the Poisson  distribution model is greater than one, then the NB distribution might fit the data better. The Poisson distribution was then used as a first step to develop the models.  Table 5.1 presents a comparative analysis between the two  approaches and their results. Note that the dispersion parameter is greater than one and relatively high for the majority of the models, ranging from 1.03 for the Accident Type 7model, to 8.06 for the Total Accidents model. These high values can be explained by the lack of significance of the Pearson x test which 2  indicates that the data has greater dispersion than can be explained by the Poisson error structure and suggests the use of NB error structure. Calculations for the NB distribution show that Od range from 0.73 for the Total Accidents model to 0.98 for the Accident Type 5 model indicating that the data dispersion is satisfactorily explained by the NB distribution. In addition, other parameters can be observed in Table 5.1. The scaled deviance is considerably greater and it exceeds the degrees of freedom by far for all the Poisson models, while the NB models have close values for the scaled deviance and the degrees of freedom indicating a better fit. Also, the percentage of closer estimates by both models is presented, these percentages suggest that for the majority of the models the NB distribution fits the data better.  45  M i c r o s c o p i c .Acckienl' P r e d i c t i o n Models i'or S i g n a l i s e d i n t e r s e c t i o n s  APM PARAMETERS  <x  1i  Pi Ih  3.20 K  SD dof  Pearson  •/  (95%)  Closer  Est.  APM  "o /•'/  r o  p  SD dof Pearson •  •/  (95%)  •'"loser Est. APM  I I  618.54 168 602.00 198 45%  5.56 179.70 168 167.70 198 55%  606.55 167 641.40 197 53%  0.41 0.90 0.12 6.24  Poisson NB 0.56 0.30 0.83 0.93 0.14 0.24 1.04 0.88  1091.50 167 1166.00 197 51%  2.16 192.92 1129.10 167 167 165.10 195.00 197 197 52% 49%  Poisson  Poisson NB 0.03 2.60 0.60 0.32 0.53 0.15 1.03 0.98  3.59 187.85 167 183.70 197 47%  TOTAL  182.96 166 193.20 196 38%  NB 0.03 0.61 0.55 0.91 4.58 161.68 166 170.90 196 62%  RT  LT  AT 13  PARAMETERS  i  Poisson NB 2.30 0.04 0.34 1.52 0.17 3.43 0.89  Poisson  0.05 1.49  p  AT7  ATS  ATI  Poisson NB •:-}4NB:;M 0.17 0.21 0.44 0.47 0.46 0.85 0.55 0.45 0.25 0.85 1.72 0.89 2.14 2.24 189.80 343.32 192.13 167 167 167 165.60 322.00 159.20 197 197 197 54% 46% 48%>  SEVERE NB Poisson  PD O Poisson  NB NB 0.50 0.61 0.76 1.05 1.46 1.75 <(o 0.72 0.67 0.73 0.68 0.68 0.66 ••» 0.34 0.33 0.49 0.43 0.44 0.39 /•*_. 0.80 3.21 0.75 6.28 0.73 8.06 4.86 4.73 5.06 1640.30 179.53 1273.10 181.22 644.17 185.69 SD ••• 167 167 165 165 167 167 dof 1508.00 136.30 1174.00 140.60 599.40 150.10 "•'"' Pearson 195 195 197 197 / (95%) 197 197 44% 56%o 53% 47% 51% 49% Closer Est. Table 5.1Comparison between the Poisson and the NB distributions PARAMETERS  Poisson  •  \  •  eis  ;  16  M i c r o s c o p i c Acdikai  P r e d i c t i o n M o d e l * for Signalised Intersections  6. APPLICATIONS 6.1 Introduction Five applications of APMs are discussed in this chapter. The first application relates to using the Empirical Bayes approach to obtain refined safety estimates. The second application refers to the identification of APLs. The third application refers to the ranking the identified APLs, and the fourth relates to before and after safety evaluations. A comparison between the use of microscopic and macroscopic models is provided.  6.2 Location specific prediction: EB safety estimates As reviewed in the Previous Work section, the main objective of using the EB refinement method is to yield more accurate, location specific safety estimates by combining the observed number of accidents at the location with the predicted number of accidents from the APMs. Consider the following volume and accident data:  47  IVlfrrosconk Arxuieirt Prediction Models for Signalised intersections  MOVEMENT  AADTK  EBL  1247.5  EBT EBR WBL WBT WBR NBL NBT  9910.3 1024.5 738.7 9506.1 1832.9 725.2 11343.5  i\!3R S3L SBT  663.6 1183.6 9571.5  SBR  1874.6  Table 6.1 Example volume data AccidentAccident Type count 27 ATI At 4  AT 7 Alls RT  toiAL SEVERE  PUU  17 3 38 40 5 103 78 25  Table 6.2 Accidents / 3 years Step 1. The flows needed for each APMs are calculated from the detailed volumes (see to Table 3.1): VOLUMES ( A A D T / 1 0 0 0 ) !  F  .  MICROSCOPIC A P M s bv AT 49 62 1, 4, 6 21.25 25.36 20.91 40.33 7, 8, ? , 1 0 40.33 1 1 , 12, 1 3  19.08 24.26 19.42 5.40 3.90  MACROSCOPIC APMs 25.36 TOTAL, S E V E R E , and PDO 40.33 "' LEFT TURN 40.33 R I G H T TURN  24.26 3.90 5.40  Table 6.3 APMs Flows  48  Microscopic- Aeeiaeat Prediction Models f « r Signalised intersections  Step 2. Calculate the predicted number of accidents using the APM and its variance (equation 2.4). Sample calculations in equation form are shown for the total model only, see Table 6.4 for the rest of the results.  6790 „  E(A) = 1.4592 x 25.23  Var(E(A))  53  -i £ . 4 3 8 7  x 24.26 0  /  1  = 53.\0acc/3years  10  2  = d±l2L. = 556.84(acc I'3yearsf  5.064  Step 3.  Obtain the empirical safety estimate its variance for the  intersection using equations 2.18 and 2.19  f  FB  safety estimate  Var(EB  53.10 (5.064 + 103) = 98.66acc 13years 5.064 + 53.10 \  safclv safety  A  )=  estimate  f  53.10 ^ (5.064 + 103) = 90.07 (acc/3years) 5.064 + 53.10  1  Figure 6.1 shows the Observed number of accidents plotted versus the Predicted number of accidents and the EB refined number of accidents. It can be observed how the EB refined estimate is closer to the Observed number of accidents than the Predicted number of accidents. The EB refined estimates are closer to the 45° line than the Predicted number of accidents.  49  Microscopic Accident Prediction Models lor Signalised  Accident 1 Type  intersections  APrfs  APMs  Accidents / 3 years  .it, /  iyens  Var(EB )  EB  se  se  : acc/3  years  1.5210  ATI  0.0418 x V  MICROSCOPIC APMs  0.3177  ATS  2.5971 x MA  AT ;<  10.0283 x T 0.9327  AT  13  0.3027 x T  LT  0.4423 x T  MACROSCOPIC APMs  RT  0.1703 x T  1.4592 x MA  SEVERE1 0.7628  x MA  PDO 10.4958 x MA  11.52  0.69  0.99  0.13  13.27  34.54  29.70  14.35  36.53  31.60  2.47  3.83  2.05  53.10  98.66  90.07  39.10  73.80  65.84  14.82  22.49  16.93  0.5468  0.4387  0.4914 x MI  0.7152  15.40  0.2532  x MI 0.7330  10.65  0.2441  x RT 0.6790  TOTAL  x RT  x LT 0.4740  17.85  0.5462  x LT 0.8480  24.11  0.1501 x MI  0.6149  15.86  0.3402  x MI  Table 6.4 Predicted number of accidents and EB safety estimates  50  CO  o vO o  o  <  fNJ  4<  o o  <  o  CO  o vO o  o  fNJ  o o o o o o o o o C O v O ^ r v J O C O v D ^ f N  o  (sjeaA £ / 3 D B )  siuappDe jo jaquinu pauijsj  93 ' p a p i p a J d  (Vlieroscopie A c c i d e n t P r e d i c t i o n Models f o r Signalised I n t e r s e c t i o n s  6.3 Identification of APLs As mentioned earlier, APLs are locations that exhibit a significant number of accidents compared to a specific "norm". This "norm" is usually accepted as the 50 percentile of the prior distribution. For the example given in the previous th  section, the following steps are followed to identify APLs •  Calculate P using Equation 2.21 50  j(5.064/53.10) o  •l -' r(5.064)  5064  ( 5 0 6 4  )  •^  ( 5 Q 6 4 / 5 3 1 0 )  "^  l  - Q  5  Solving the integral for 0.5, P o = 49.65 accidents / 3 years. Table 6.5 5  shows the P values for the other models. 50  52  M i c r o s c o p i c Aa'.UIent P r e s l k t i o n M o d e l s f o r Signalised intersections  Accident Type  APMs Accidents / 3 years  APMs . Var (APMs )  Pso  e  wicc/3  ye.irs  (Ml/3yCMS}  m  .ict.-3  ymiis  1.5210  ATI  0.0-118 x V  MICROSCOPIC APMs  0.3177  ATS  x MI 0.6149  0.0283 x T  x RT  x LT 0.8480  Ll  x LT 0.4740  MACROSCOPIC APMs  x RT 0.6790  x MI 0.7330  0.79  0.64  13.27  94.74  11.28  14.35  106.20  12.28  2.47  5.33  2.10  53.10  609.94  49.65  39.10  362.48  36.38  14.82  60.02  13.81  0.4914  x MA  x MI 0.7152  PDO  0.69  0.4387  1.4592 x MA  SEVERE0.7628  9.68  0.5468  0.1703 x T  TOTAL  42.26  0.2532  0.4423 x T  RT  10.65  0.2441  0.30;->7 x T  13  14.92  0.5462  0.9327  AT  61.08  0.1501  2.5971 x MA  AT 7  15.86  0.3402  0.4958 x MA  x MI  Table 6.5 P o calculations 5  Calculate the probability of exceeding P o using equation 2.24 5  49.65  (5 0 6 4 / 5 3 10 + 1 ) ' °  J  ( 5  6 4 + 1 0 3 )  . ^(  5 0 6 4 + 1 0 3  r ( 5 . 0 6 4 + 103)  - ) .g-(5.064/53.10+l)A 1  dZ = 0.9999  This result indicates that this intersection can be regarded as an APL according to the total accident model because there is significant probability, 99.9999%, of exceeding the P  50  value.  Figure 6.2 illustrates the APL  identification procedure for this example. Table 6.6 shows the probabilities of exceeding P according to the rest of the models. The last column indicates 50  53  M i c r o s c o p i c A c c i d e n t P r e d i c t i o n Models f o r Signalised I n t e r s e c t i o n s  whether the intersection is APL or not according to each accident type (at the 95% confidence interval).  Prior and Posterior Distributions  Posterior  /  I  Probability of safety estimate less than Pso=0.999999999907166  /  'Prior " Posterior  \  \ \  Prior/^  Pso=49.65  15  30  60  75  90  105  120  135  150  Accidents / 3 years  Figure 6.2 APL identification procedure  54  Accident  APMi ,,.  Type  .iter iyt. r  Var (APMs  J  f  P. .icr J yturs  Probability  APL  1.5210 Al 1  0 0418 x  \l  APMs  Al 5  2 5971 x MA  MICROSCOPIC  0.3177  AT7  0 0283 x  T  •a 13  0.3027 x  T  LT  0.4423 x  T  KJ  0.1703 x T  x  MI  0.9327  MACROSCOPIC APMs  0.6790  x MA  9.68  96.99417974  APL  0.69  0.10  0.64  84.09279633  NO  13.27  81.48  11.28  99.99999984  APL  14.35  91.85  12.28  99.99999986  APL  2.47  2.86  2.10  90.82578431  NO  53.10  556.84  49.65  99.99999999  APL  39.10  323.38  36.38  99.99999946  APL  14.82  45.20  13.81  99.17899953  APL  0.4914 x MI  0.7152 0.4958  31.61  0.4387 x MI  0.7330  PDO  10.65  0.5468 x RT  x MA  APL  0.2532 x LT  0.4740  0.7628  99.33492696  0.2441 x LT  0.8480  SEVERF  14.92  0.5462 x RT  1.4592 x MA  45.23  0.1501  0.6149  TOTAL  15.86  0.3402 x MI  Table 6.6 APL according to the first method P o 5  It can be seen in Table 6.6 that, at the 95% confidence level, the intersection is an APL according to the microscopic models for AT 1 (rear end), AT 5 (intersection 90°), AT 13 (one of the three left turn types), and macroscopic models for Total, Severe, PDO, and Left Turn accidents. However, it is not an APL according to AT 7 (one of the four right turn types), and combined Right Turn.  6.4 APLs Ranking Two ranking criteria can be used to rank APLs (Rodriguez and Sayed, 1999). The first consists of calculating the difference between the EB estimates and the predicted frequency for the previously identified APLs. This is a good indication of the expected safety benefits and is helpful in carrying out the estimation of the pre-implementation safety benefits that are going to be obtained from implementing the countermeasure. For the second criterion the  55  r v i k n w e o j d e Accident P r e d i e f i o o Models IVir Signalised Intersections  ratio between the EB estimate and the predicted frequency has to be calculated. This ratio represents the deviation of the intersection from the "norm". In other words, the higher the ratio, the more accident prone the intersection is. The first criterion is useful to indicate potential economical benefits and the second guarantees that the safety level at each intersection is comparable to the other intersection with similar characteristics. A comparison of the two ranking criteria is shown in Table 6.7. Using the first method 48 intersections were identified as APLs at the 95% confidence interval. The table shows the calculations needed (EB - Predicted and EB / Predicted) and the ranking according each criteria, the difference in ranking is also included. The difference in rank between the two criteria ranges from 0 to 25, having an average of 6.29. This difference can be explained by the different goals sought by each criterion.  While the first criterion tends to favour  intersections that exhibit high accident occurrence which are usually more cost effective to treat, and the second considers the deviation from the expected values and its variance regardless of the number of accidents observed. Road authorities can consider the second criterion to ensure that the safety of different locations is within acceptable levels.  56  Mfcrwseoitio ' • *«{••  .'.-.--•iefioM Models, for Siana.iis.eil i n t e r s e c t i o n s  Rank Difference; Rank EB / Prcd EB F.B - Prod Observed Piedictud in EB - Prod EB / Prod Accidents Accidents (•stiruitcs Rank acc ; 3 voars acc / 3 years acc ;' 3 years acc / 3 years acc / 3 years 1 2 1 64.82 2.13 1 57.46 122.29 128 1 1 2 82.71 2.16 154.14 160 71.43 2 6 3 1.82 3 50.87 112.84 117 61.97 3 5 4 9 1.86 45.55 1 53.10 98.66 103 0 5 1.87 5 92.57 43.17 97 49.40 5 4 2 2.13 6 52.29 27.72 24.57 6 58 7 8 1 1.81 34.87 82 43.03 77.90 3 5 8 1.68 79.94 134.54 54.60 C 138 7 2 1.74 9 54.62 95.23 40.61 99 9 1.42 10 16 6 35.39 84.49 119.88 10 122 1.58 11 10 1 71.07 26.05 11 74 45.02 20 8 12 27.07 1.38 72.02 99.10 12 101 2 15 1.42 13 24.66 58.20 82.85 13 85 4 14 18 22.14 1.40 14 79 54.81 76.95 21 6 1.37 15 21.47 57.64 79.11 81 15 16 14 2 1.48 17.46 53.55 16 56 36.09 13 4 1.49 17 17.27 52.52 35.25 17 55 23 5 1.36 18 21.96 61.22 83.18 85 18 17 2 1.41 19 18.79 64.94 67 46.15 19 20 28 8 22.54 1.31 96.46 20 98 73.91 11 10 21 13.57 1.55 38.21 41 24.64 21 1.29 22 29 7 22.21 98.53 76.31 22 100 33 10 1.27 23 23.06 108.64 85.58 23 110 24 36 12 1.25 22.14 111.75 89.61 24 113 25 12 13 1.52 12.48 23.87 36.35 39 25 34 8 26 1.26 21.35 83.35 104.70 106 26 27 25 2 1.33 17.43 70.33 72 52.91 27 26 2 1.33 28 16.62 67.34 50.72 69 . 28 41 12 29 1.23 107.84 20.11 87.73 109 29 35 5 30 1.25 19.01 75.72 94.73 96 30 37 6 1.24 31 82.78 16.05 84 66.74 31 32 30 2 1.28 12.49 56.57 44.08 58 32 42 9 1.22 33 14.56 66.32 80.89 82 ->3'~40 6 34 1.23 13.05 56.78 69.84 71 34 24 11 35 8.87 1.35 25.36 34.23 36 :s 19 17 36 1.38 8.06 21.00 29.06 31 36 32 5 37 1.28 10.39 47.59 37.19 37 49 44 6 1.22 38 11.75 65.90 67 54.15 = 38 6 45 39 1.20 12.20 74.00 61.80 75 439; ; 43 3 1.22 40 11.04 61.90 50.86 63 '-4 o; 47 6 41 1.17 11.90 80.12 68.22 81 41 42 48 6 1.16 12.24 87.17 74.93 42 88 43 27 16 1.31 8.17 34.42 26.25 4> 36 39 5 44 1.24 9.87 51.81 41.94 53 44 45 38 7 1.24 8.56 44.80 36.25 46 45 31 15 46 1.28 7.34 33.58 26.24 35 46 22 25 47 1.36 5.61 21.17 15.56 23 47 48 46 2 1.20 8.67 53.01 44.34 54 48 INT No  Table 6.7 Ranking according to the first method used to select APLs  57  M k ' n s s e opto A c c i d e n t P r e d i c t i o n M o d e l s l o r Signalised i n t e r s e c t i o n s  Alternatively accident priorities can be assigned according to the severity of each accident type, the average estimated accident cost, the specific accidents that are occurring at the intersection, or the effectiveness of the viable countermeasures that can be implemented at the intersection.  6.5 Critical Accident Frequency Curves The process of identifying accident prone locations requires considerable computational effort. To facilitate this process, critical accident frequency curves can be developed for any APM. A critical curve can be defined as the threshold that must be exceeded by the observed number of accidents in order to classify the location as APL for a given APM at a certain confidence level. The procedure to obtain these curves is iterative and follows the steps employed to identify APLs. The predicted number of accidents for any given APM and Kparameter is calculated and used as initial guess. Then, the P is 50  calculated as in equation 2.21. After this is done, the P o value is used to identify 5  the location as APL if the confidence level is exceeded as in equation 2.24. This procedure is repeated until the observed number of accidents yields to the confidence level desired. This means that the procedure is repeated until a count variable fits the level of confidence sought.  The critical curve is then  obtained by joining all the critical points in a Predicted versus Observed accidents graph. As an example, the following figures show the critical curves for all models developed for this thesis.  Three curves are shown in each figure  representing the 90%, 95%, and 99% confidence levels.  In addition to the  curves, the predicted versus observed number of accidents is shown. A triangle mark denotes an APL at the 99% confidence level and circle marks an intersection that is not accident prone at the same confidence level.  58  Microscopic Accident Pi r d k i i o n M o d e l * for Signalised Intersections  Figure 6.3 Critical Curve for AT1  59  Microscopic  etHretmii Models for Signalised Intersections  Figure 6.4 Critical Curve for AT5  Critical Curve for AT5  60  Microscopic \ c e « l r a i P r e d i c t i o n M o d e l * f o r S i g n a l i s e d I n t e r s e c t i o n s  Figure 6.5 Critical Curve for AT7  Critical Curve AT7  61  Microscopic A c c i d e n t P r e d i c t i o n M o d e l s t o r S i g n a l l e d I n t e r s e c t i o n s  Figure 6.6 Critical Curve for AT13  Critical Curve for AT13  V l i r r o K C D i i i c A m d e i i t Prediction Model* for Signalised Intersections  Figure 6.7 Critical Curve for Left Turn Accidents  Critical Curve for Left Turn Accidents  63  M i c r o s c o p i c Accident Prediction Models for Signalised Itii.i-ectktns  Figure 6.8 Critical Curve for Right Turn Accidents  64  MBawwcaplc A c c i d e n t P r e d i c t i o n M o d e l s f o r S i g n a l i s e d  Intersections  F i g u r e 6.9 C r i t i c a l C u r v e f o r T o t a l A c c i d e n t s  Critical Curve for Total Accidents 175 150  99%  95% 90%  20  40  60  80  100  Predicted Accidents  90% — 95% — 99%  * APLs  65  Microscopic- A c c i d e n t P r e d i c t i o n M o d e l s f o r Signalised i n t e r s e c t i o n s  Figure 6.10 Critical Curve for Severe Accidents  Critical Curve for Severe Accidents  66  V l k r o s c o n k AeekleiM P r e d i c t i o n M o d e l s IVir S i g n a l i s e d I n t e r s e c t i o n s  Figure 6.11 Critical Curve for PDO Accidents  Critical Curve for PDO Accidents 95%  3  6  9  12  15  18  21  24  27  30  Predicted Accidents  90% — 95% — 99%  * APLs  67  To illustrate the use these curves, consider the example described before. Using the APM developed for this thesis, the predicted number of accidents for the given traffic volumes can be obtained as done for Table 6.4. For this number of accidents and for, lets say, the 95% confidence level the critical threshold should be obtained as described before. Calculations for the critical threshold are summarised in Table 6.8. The observed number of accidents, actual count, is then used to identify the location as accident prone if this value surpasses the threshold for the desired confidence level.  ACTiJAL COUNT  APL  APMs  THRESHOLD COJNT 95%  ATI  22.7331  27  APL  ATS  16.0249  17  APL  AT7  4.6917  3  NO  AT13  17.6772  38  APL  LT  18.9152  40  APL  RT  5.7640  5  NO  & r  TOT  62.2219  103  APL  w  SEV  47.3256  78  APL  PDO  21.2895  25  APL  u ©  u o §£ u  t=a  O  ,.  O U  Table 6.8 Number of Accident Prone Locations  68  Table 6.9 shows the number of APLs identified by the APM developed for this thesis for different significance levels.  APMs  COM FIDE6\SCE L EVE I 90% 95% 99%  ATI  33  21  13  AT 5  37  28  13  u  2  0  0  w  49  34  27  48  41  27  21  12  5  58  48  33  SEV  55  41  21  PDO  39  32  17  5 RT  u Q.  ©  u 0  , TOT  Table 6.9 Number of Accident Prone Locations  Furthermore, additional curves can be developed in a more general way for various K values. The main advantage of these curves is that they can be used for any negative binomial model. However, the results obtained from the use of these general curves are not as accurate as the results obtained by using the previous curves. 69  1 Inlersecrioos  Microscopic Aecklesit Prediction Models  Figure 6.12 Critical Curves for various K values  Critical Curves for different k 250  0  40  80  120  160  200  Predicted Accidents  0.5  1  2  5  10  25  50  100  70  M i c r o s c o p i c A e e i d e i s f P r e d i c t i o n M o d e l s f o r Sco'iaiis-eil Irticrsecfiofts  6.6 Before and After studies The benefits obtained from safety improvement programs are usually represented by the reduction in the number of collisions after implementing the program.  To estimate this reduction, a comparison between the observed  collision frequency after treatment and the expected collision frequency had no treatment taken place is undertaken.  The easiest way to conduct this  comparison is the simple before and after comparison method. In this method, a simple comparison between the number of collisions before and after treatment is undertaken using statistics such as the two sample T-test.  The main  assumption of this method is that the number of collisions would not change had no treatment taken place. However, because of the randomness inherent to accident occurrence (e.g. regression to the mean effect, accident migration, etc.), the observed number of accidents may not be a good estimate of what could have happened had no countermeasure been implemented. The EB refinement process can be used to overcome this limitation.  To illustrate this, the example above is continued. To keep calculations simple, assume that traffic flows at the intersection remained unchanged. Suppose further that a countermeasure to reduce the number of rear end accidents (e.g. all red interval) was implemented and the observed number of accidents in a three year period after the implementation is 88. Thus, the safety would have been had the treatment not been implemented on the intersection can be calculated as:  YdC  MoE = \ - ^ ~ pac  (6.1)  Where,  71  Microscopic Accident Prediction Models for Signalised intersections  MoE  = Measure of Effectiveness  rac  = accidents count after implementation  pac  - previously observed accident count (before implementation) oo  MoE = \ - ~  103  = 0.14  Hence, in regard to the number of total accidents, it can be said that the countermeasure yielded a positive effect; it decreased the number of accidents at the intersection by 14%.  6.7 Recommended Research A lack of detailed data to develop the APMs was found in the literature review and during the course of this thesis.  It was observed that current  microscopic models just take into account the traffic flows of the colliding vehicles but do not include other important factors that may contribute to traffic accidents. It is recommended that the models'developed in this thesis be further refined by adding Intersection Layout variables (e.g. number of lanes of each road, number of left and right turn lanes, pedestrian crosswalks, speed limit, etc). The addition of these variables should enhance the predictive ability of the models and contribute to improve our understanding of the relationships between accident occurrence and geometric design.  72  Microscopic A c c i d e n t Prediction Models for Signalised intersections  7.CONCLUSIONS This thesis summarises the results of a study undertaken to develop Microscopic Accident Prediction Models, APMs, for 4-leg urban signalised intersections based on data from the City of Vancouver, B. C. The Generalised Linear Interactive Modelling approach (GLIM) was used to overcome the shortcomings associated to the Conventional Linear Regression approach. The GLIM approach allows using non-normal (Poisson or Negative Binomial) error structure and the use of nonlinear relationships in the models. A comparison between the results obtained by models developed using the Poisson versus the Negative Binomial (NB) error structures was undertaken. It was observed that for the majority of the models the NB error structure provided closer safety estimates. Microscopic models for the accident types described in the MV 104 form, British Columbia's accident police report, were developed. Also Macroscopic models for total, severe and injury accidents were developed. Several statistical tests were applied to all developed models to determine their reliability. After applying the tests, six microscopic models were statistically significant. All macroscopic models were significant. Several applications were covered and a discussion regarding their use was provided. The Empirical Bayes, EB, method was used to overcome the problems associated with the regression to the mean phenomenon and to provide a more accurate site-specific safety estimates. The process to identify APLs, locations that exhibit a significant number of accidents compared to a specific "norm", was explained in detail. Two ranking criteria were used to rank the identified APLs. The first criterion is useful to indicate potential economical benefits and the second attempts to ensure that the safety level at each intersection is comparable to other intersection with similar characteristics. To  73  facilitate the APLs identification process, critical accident frequency curves were developed for each APM. A critical curve can be defined as the threshold that must be exceeded by the observed number of accidents in order to classify the location as APL for a given APM at a certain confidence level. The use of the models to perform before and after safety evaluations was described. A detailed example that includes the specific traffic flow calculations and all the applications previously discussed was provided for all statistically significant models. It was observed that the use of detailed microscopic accident prediction models could be beneficial when identifying accident prone locations that can be successfully treated. Microscopic models allow to identify locations that are accident prone to specific accident type(s) that can be effectively treated by some specific countermeasure that permits reducing that specific accident type as opposed to macroscopic models that don not provide such detail. By using microscopic models the safety of all road network users and the benefits of road improvement programs can be maximised.  74  M i c r o s c o p i c Accislcisf Predfctioss Models i'or SigmilistMl i n t c r s a c f i o n s  BIBLIOGRAPHY Al-Turk, M. and Moussavi, M., (1996). 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"Safety effects of cross section design for two lane roads", Volume I, Final Report, FHWA-RD-87/008, Federal Highway Administration, Transportation Research Board  78  ikm  IVedietinn Models for SfensSised Intel-sections  APPENDIX I INTERSECTION LIST  79  Microscopic A c c i d e n t Prediction M o d e l s f o r Signalised i n t e r s e c t i o n s  l|f||W^lllli INTERSECTION LIST 10th and 10th and 10th and 12th and 12th and 12th and 12th and 12th and 12th and 12th and 12th and 12th and 12th and 12th and 12th and 12th and 16th and 16th and 16th and 16th and 16th and 16th and 16th and 16th and 16th and 16th and 1st and 1st and 1st and 1st and 1st and 1st and 22nd and 22nd and 22nd and 33rd and 33rd and 33rd and 33rd and 33rd and 33rd and 33rd and 33rd and 33rd and 41st and 41st and 41st and 41st and 41st and 41st and 41st and 41st and 41st and 41st and 41st and 41st and  Ash Blanca Macdonald Arbutus Burrard Cambie Clark Commercial Fir Fraser Granville Heather Hemlock Kingsway Main Oak Artubus Blenheim Burrard Cambie Fir Fraser Granville Macdonald Main Oak Clark Commercial Main Nanaimo Renfrew Rupert Boundary Renfrew Rupert Arbutus Cambie Fraser Granville Knight Main Nanaimo Oak Victoria Blenheim Cambie Clarendon Dunbar Fraser Granville Knight Main Oak Rupert Victoria W. Boulevard  Table AI.1  Boundary 49th and 49th and Cambie Elliott and 49th Fraser 49th and Granville 49th and Kerr 49th and and Knight 49th Main 49th and Oak 49th and Tyne 49th and 49th and Victoria 49th and W. Boulevard 4th and Alma Arbutus 4th and and Blanca 4th Burrard 4th and Fir 4th and Macdonald and 4th and Main 4th Elliott 54th and Kerr 54th and Victoria 54th and Fraser 57th and Knight 57th and Main and 57th Clark 6th and Granville 70th and Oak 70th and 70th and SW Marine Dr. 10th Ave Alma and and Broadway Alma Broadway Arbutus and Arbutus and Cornwall Ave Arbutus and King Edward Ave. 10th Blenheim and Blenheim and Broadway Blenheim and King Edward Ave. S.W. Marine Blenheim and 1st Boundary and 29th Boundary and Boundary and Grandview Hastings Boundary and Boundary and Kingsway Boundary and Lougheed Boundary and Marine Moscrop Boundary and Boundary and Rumble Burrard Broadway and Cambie Broadway and Clark Broadway and Commercial Broadway and Fir Broadway and Fraser Broadway and Granville Broadway and Broadway and Macdonald Broadway and Main Broadway and Nanaimo Broadway and Oak Broadway and Renfrew Broadway and Rupert  Cambie Cambie Commercial Commercial Commercial Cordova Dunbar St Dunbar St Dunbar St Earles Earles Earles Fraser Fraser Grandview Granville Granville Hastings Hastings Hastings Hastings Joyce Kerr King Edward Ave. King Edward Ave. King Edward Ave. King Edward Ave. Kingsway Kingsway Kingsway Kingsway Kingsway Kingsway Kingsway Macdonald Main Main Main Main Main Marine Dr Marine Dr Marine Dr McGill Nanaimo Nanaimo Oak Oak Point Grey Renfrew Rupert Rupert Slocan Venables  and King Edward Ave. and Marine and Hastings and Grandview and Venables and Main and 16th and King Edward Ave. and S.W. Marine and 29th Ave and 41st Ave and Kingsway and King Edward Ave. and Marine and Victoria and King Edward and Park and Clark and Main and Nanaimo and Renfrew and Kingsway and S.E. Marine and Knight and Macdonald and Main and Oak and Broadway and Fraser and Knight and Nanaimo and Rupert and Slocan and Victoria and Kitsilano and 2nd and Marine and Powell and Prior and Terminal and Cornish and Oak and Victoria and Renfrew and Grandview and Dundas and 19th and Park and Alma and Grandview and 29th and Grandview and 29th and Clark  Intersection List  80  APPENDIX II DATA ANALYSIS  M i c r o s c o p i c A c x k i e i i t P r e d i c t i o n Mosleis l o r S i g n a l i s e d i n t e r s e c t i o n s  HOUR 0:00 TO 0:59 1:00 TO.1:59 2:00 T O 2:59 3:00 TO 3:59 4:00 TO 4:59 5:00 T O 5:59 6:00 TO 6:59 7:00 TO 7:59 8:00 TO 8:59 9:00 TO 9:59 10:00 TO 10:59 11:00 TO 11:59 12:00 TO 12:59 13:00 TO 13:59 14:00 TO 14:59 15:00 TO 15:59 16:00 TO 16:59 17:00 TO 17:59 18:00 TO 18:59 19:00 TO 19:59 20:00 TO 20:59 21:00 TO 21:59 22:00 TO 22:59 23:00 TO 23:59 TOTALS  MAX MIN AVERAGE STDEV MEDIAN VAR Table Al 1.1  FREQUENCY  %  195 2.30 143 1.69 140 1.65 55 0.65 40 0.47 41 0.48 174 2.06 290 3.43 460 5.43 384 4.54 428 5.06 462 5.46 483 5.71 453 5.35 539 6.37 580 6.85 616 7.28 580 6.85 601 7.10 549 6.48 357 4.22 365 4.31 287 3.39 244 2.88 8466 100 616 40 352.75 188.19 374.50 35413.67  Time of Accidents  82  JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC TOTALS  % TOTAL  MAX MIN AVERAGE STDEV MEDIAN VAR  Table All.2  1996 1995 1994 0/ COUNT % COUNT COUNT % /o 8.10 210 8.82 9.14 270 257 7.48 256 9.87 229 9.25 260 9.24 253 9.76 9.04 283 254 7.45 239 9.22 228 7.47 210 7.77 207 7.98 238 7.76 218 7.12 178 6.86 218 8.00 225 8.10 213 8.21 248 8.11 228 8.33 192 7.40 255 4.41 124 8.92 199 7.67 273 6.69 188 256 9.87 8.46 259 289 10.28 8.10 9.37 210 287 283 10.07 274 8.95 180 6.94 9.78 275 2593 100.00 3062 100.00 2811 100.00 33.20 8466 289 124 234.25 46.59 241.00 2170.57  36.17  30.63  287 218 255.17 23.05 257.00 531.42  256 178 216.08 28.39 210.00 805.90  Date of Accidents  M i c r t w e o n k A c c i d e n t Prediction Models for Signalised Intersections  CONTRIBUTING CIRCUMSTANCES  TOTAL  %  ALCOHOL AVOID P/V/B BACK UNSAFE BLACKOUT CUT IN DEF BRAKE DEF BRKLIGH DEF HDLIGHT DRIV INATT DRIV INEXP FAIL SIG FAIL YIELD FATIGUE FELL ASLEEP GLARE-ARTIF GLARE-SUN IGNR OFFICR I G N R T R CON ILL DRUGS ILLNESS IMPROP PASS IMPROPTURN INSECURE LD MEDICATION OTHER OVERSIZE PED ERROR PREVACCID ROAD DEBRIS SPEEDING TIRE FAIL TOO CLOSE VIS IMPAIRD WEATHER WRONG SIDE REPORTED  214 6 8 4 17 11 1 3 282 72 4 789 4 1 1 13 3 499 1 7 11 44 1 1 490 3 2 1 3 60 2 96 18 55 2  2.53 0.07 0.09 0.05 0.20 0.13 0.01 0.04 3.33 0.85 0.05 9.32 0.05 0.01 0.01 0.15 0.04 5.89 0.01 0.08 0.13 0.52 0.01 0.01 5.79 0.04 0.02 0.01 0.04 0.71 0.02 1.13 0.21 0.65 0.02  2729  UNREPORTED  5737  32.23 67.77  TOTAL  8466  100.00  Table All.3  Contributing Circumstances  diction Models for Signalised Intersections  A CCIDENT INFORMA TION  TOTALS AT 1 AT2 AT3 AT 4 AT5 AT6 ATI AT8 AT9 AT10 AT11 AT12 AT 13 AT 14 AT 15 AT16 OTHER Total LT's RT's Total Fatal Injury PDO Total Table All.4  2692 497 0 45 1655 16 107 79 153 33 92 91 2202 0 1 0 803 8466 2385 372 2757 10 5963 2493 8466  MAX MIN AVERAGE STDEV MEDIAN VAR % 15.84 11.65 13 135.7 0 53 31.80 5.87 17 0 2.92 2.66 2.5 7.1 0.00 0 0 0.00 0.00 0 0.0 0.53 2 0 0.26 0.54 0 0.3 19.55 37 0 9.74 6.88 8.5 47.3 2 0 0.09 0.31 0 0.1 0.19 1.26 4 0 0.63 0.89 0 0.8 0.93 4 0 0.46 0.84 0 0.7 5 0 0.90 1.20 0 1.4 1.81 3 0 0.19 0.51 0 0.3 0.39 0 0.54 0.94 0 0.9 6 1.09 1.07 3 0 0.54 0.75 0 0.6 62 0 12.95 11.29 10 127.6 26.01 0 0 0.00 0.00 0 0.0 0.00 1 0 0.01 0.08 0 0.0 0.01 0.00 0 0 0.00 0.00 0 0.0 9.48 23 0 4.72 3.40 4 11.5 100.00 11.79 11 139.1 14.03 0 64 86.51 13.49 11 0 2.19 2.19 2 4.8 100.00 0 0.1 0.24 0.06 1 0 0.12 70.43 120 1 35.08 22.27 33 496.0 29.45 53 0 14.66 9.31 14 86.6 100.00  Accident Information  85  M i c r o s c o p i c AceStteitt P r e d i c t i o n M o d e l s f o r Si&»alb*<$ l o l c r s e c i i o n s  DARK TOTAL %  MAX MIN AVERAGE STDEV MEDIAN VAR Table All.5  %*  MAX MIN AVERAGE STDEV MEDIAN VAR  LIGHT CONDITION DAY DK-I DUSK 629.00  20.00  20.16  0.24  7.00  32.00 0.00  2.00 0.00  1.51  10.04  0.12  19.53  129.97  1.56  9.00 16892.81  0.00  5732.00 67.71  2.00  5.00  107.00  7.43 14.00  0.00  0.00  2.00  0.00  0.16  0.56  33.72  3.70  436.26  47.89  30.00 190326.40  3.00 2293.72  1.00 381.28  0.00 4.37  7.28 0.00 53.04  0.00  I  UNKN  1707.00  95.00 1.12  2.09  ILLU  256.00 3.02  27.00 0.32  2.44  I  UNREPORTED  0  Light Conditions  DRY TOTAL  DAWN  ICE  ROAD CONDITION MUD SLUSH SNOW  WET  12.00  55.00  3035.00  5311.00  16.00  12.00  62.92  0.19  0.14  0.14  0.65  35.96 73.00  97.00  1.00  1.00  2.00  4.00  0.00  0.00  0.00  0.00  0.00  0.00  31.24  0.09  0.07  0.07  0.32  17.85  404.22  1.25  0.95  0.96  4.23  231.02  27.00  0.00  0.00  0.00  0.00  17.00  163397.52  1.56  0.90  0.92  17.87  53371.56  I  UNREPORTED  I  25  * Calculations according to reported accidents only  Table All.6  TOTAL %  MAX MIN AVERAGE STDEV MEDIAN VAR Table All.7  Road Conditions  CLOUDY 1375.00  WEA THER CONDITION RAIN SNOW UNKN CLEAR FOG 4685.00  21.00  WIND  2259.00  96.00  29.00  1.00  0.34  0.01  3.00  1.00  16.24  55.34  0.25  26.68  1.13  25.00  87.00  2.00  57.00  4.00 .  0.00  0.00  0.00  0.00  0.00  0.00  0.00  8.09  27.56  0.12  13.29  0.56  0.17  0.01  104.68  356.58  1.63  171.97  7.34  2.25  0.11  7.00 10957.15  26.00  0.00 2.67  12.00 29575.10  0.00  0.00  0.00  53.92  5.06  0.01  127148.70  I  UNREPORTED  Weather Conditions  86  M k r o s c o p k A c c i d e n t P r e d i c t i o n Models l o r Signalised i n t e r s e c t i o n s  MOVEMENT VOLUME STATISTICS AVERAGE STDEV MEDIAN MIN  MAX EL ET ER WL WT WR NL NT NR SL ST SR  15143.4 37132.9 9892.6 8506.2 28739.6 5046.8 5502.2 23299.1 5442.6 6208.9 30530.1 9205.0  Table All.8  EB NB SB  1636.4 7600.6 1593.5 1250.9 5882.3 912.0 1058.5 5659.1 987.1 1306.4 7246.4 1284.6  936.0 10315.0 1091.8 1015.3 9506.1 964.0 972.6 7523.8 902.8 1178.3 8716.6 865.0  VAR 2677953.7 57768374.9 2539230.2 1564818.1 34600983.4 831781.2 1120514.7 32025608.2 974339.3 1706784.3 52510878.3 1650089.8  Volume Data  APPROACH VOLUME STATISTICS MIN AVERAGE STDEV MEDIAN  MAX WB  6.8 160.0 53.7 6.2 395.0 13.9 12.5 9.8 27.2 5.0 4.9 15.0  1371.3 11563.1 1496.7 1337.4 9667.2 1116.4 1169.9 8161.1 1174.9 1438.8 9671.3 1226.3  38823.6 38847.0 27260.4 31046.6  Table All.9  MAX  82213.0  Table All.10  135.0 113.7 152.0 100.0  14086.9 11694.9 10327.3 12133.3  7918.1 6644.9 6179.6 7533.1  12845.0 10924.6 9618.7 10632.6  VAR 62696581.9 44154356.7 38187024.6 56748243.7  Approach Data  INTERSECTION VOLUME STATISTICS AVERAGE | STDEV MEDIAN MIN  10504.0  47787.4  18447.8  VAR  49050.5 340322842.1  Intersection Data  87  Microscopic A c c i d e n t Prediction M o d e l s t o r Xi|i»sdiseci intersections  APPENDIX IIIOUTLIERS ANALYSIS SAMPLES  88  Microscopic Accident Prediction Models for Signalised Intersections  Outliers Analysis (before removals) o) 0 . 2 5 o | £ Q  a  o  <S  0.2 0.15 0.1 0.05  —*  0  20  40  60  Observed Accidents  Figure AIII.1  Cook's distances before removals  Outliers Analysis (after removals) 6.00E-02 5.00E-02 4.00E-02  f  •  I*  3.00E-02 2.00E-02 1.00E-02  . ..............  w  . . .  4~  • 1  0.00E+00 20  40  60  Observed Accidents  Figure AIII.2  Cook's distances after 2 warranted and 2 unwarranted removals  Intersection Number  Sample Size  169 170 123 126  169 168 167 166  Table Al 11.1  Scaled Deviance 187.6 185.69 184.33 183.65  SD Drop 2.40 1.91 1.36 0.68  Cumulative SD Drop 2.40 4.31 5.67 6.35  x  2  STATUS  REMOVAL WARRANTED 2.4 REMOVAL WARRANTED 4.2 5.8 REMOVAL NOT WARRANTED 7.3 REMOVAL NOT WARRANTED  Outlier Analysis showing warranted and unwarranted removals  89  Mk't-osdJi)!  O ut I i er s A nal ysi s (befor e r emoval s)  u  8.00E-02  «  6.00E-02  u  S 4.00E-02 M  *  2.00E-02  o  O O.OOE+00 50  100  150  200  Observed Accidents  Figure All.3  Cook's distances before removals  Outliers Analysis (after 0.05  ID  I  removals)  r  004  .£ 0.03 a 0.02  _*  y  o  <S  0.01  o  •  '^SrtfrrfrF 50  100  •  150  200  Observed Accidents  Figure All 1.4  Cook's distances after 4 unwarranted removals  STATUS SD Cumulative SD I 2 intersection Sample Scaled Drop Deviance Drop Number Size 1.22 3.8 REMOVAL NOT WARRANTED 1.22 178.31 169 123 4.09 6 REMOVAL NOT WARRANTED 2.87 175.44 97 168 REMOVAL NOT WARRANTED 7.8 5.1 174.43 1.01 167 126 REMOVAL NOT WARRANTED 9.5 6.14 1.04 173.39 166 128  Table All 1.2  Outlier Analysis with unwarranted removals  90  Microscopic Accident Prediction Models for Signalised Intersections  APPENDIX IV GLIM OUTPUT SAMPLE  91  M i c r o s c o p i c Accident P r e d i c t i o n Models f o r S i g n a l i s e d I n t e r s e c t i o n s  [o] GLIM 4, update B for IBM etc. 80386 PC / DOS on 19-Aug-1999 at 21:38:05 [o] (copyright) 1992 Royal Statistical Society, London l°l [i] ? $C Accident Type 1 (Rear End)$ (ij?$Units 170$ [ij ? $Data V ACS [i]?$DinpufAT1.txt'S [ij ? SCalc LV=%log(V)$ [ij ? $Yvar AC SError P SLink 1$ [i] ? $Fit LV $D E$ [o] scaled deviance = 618.54 at cycle 4 [oj residual df - 168 [o] estimate s.e. parameter [0] 1 -3.047 0.2335 1 [oj 2 1.489 0.05809 LV joj scale parameter 1.000  [o]  [i] ? $Look %X2S [0] 602.0 [i] ? $lnput %plc 80 NEGBINS [ij ? SNumber thela=0$ [i] ? $Use NEGBIN theta SD ES [w] - model changed jwj - model changed jo) scaled deviance = 179.70 (change = -438.8) at cycle 2 [oj residual df = 168 (change = 0) [o] [0] ML Estimate of THETA = 5.560 [oj Std Error = ( 0.8789)  [o]  [o] NOTE: standard errors of fixed effects do not jo] take account of the estimation of THETA [o] [o] 2 x Log-likelihood = 10549. on 168 df [oj 2 x Full Log-likelihood = -1111..  [o]  [o] estimate s.e. parameter [o] 1 -3.175 0.3856 1 [oj 2 1.521 0.09905 LV [oj scale parameter 1.000 [o] [ij ? SLook %X2S [o] 167.7 [i] ? SExtract %Lv : %cd$ [i] ? SCalc LEVERAGE=%lv : COOK=%cd$ [i] ? SLook LEVERAGE COOKS [o] LEVERAGE COOK [oj 1 0.008848 0.0039731790 [oj 2 0.011321 0.0022354159 [oj 3 0.007134 0.0011139343 [0] 4 0.010685 0.0002563359 [o] 5 0.016471 0.0064739888 [oj 6 0.010355 0.0006460811 [oj 7 0.007666 0.0008000573 [Oj 8 0.006871 0,0020213965 [o] 9 0.006289 0.0029712832 [oj 10 0.010039 0.0028329017 [oj 11 0.006487 0.0025161246 [oj 12 0.009867 0.0018433135 [oj 13 0.018130 0.0036192173 [oj 14 0.008718 0.0001919384 [oj 15 0.006734 0.0024754717 [o] 16 0.006279 0.0047664107 [oj 17 0.006491 0.0005430502 [oj 18 0.019282 0.0003298848 [oj 19 0.011780 0.0000565374 [oj 20 0.006356 0.0000291500 [oj 21 0.010890 0.0010440752 [o] 22 0.006427 0.0250271503 [0] 23 0.010504 0.0003394376 [oj 24 0.006596 0.0004500134 [0] 25 0.007444 0.0005880118 [oj 26 0.019063 0,0058863992 [0] 27 0.012653 0.0004235143 [oj 28 0.019915 0.0356851742 [oj 29 0.016406 0,0021061262 [oj 30 0.014895 0.0000047396 [oj 31 0.011488 0.0106990291 [0] 32 0.007922 0.0084114727 [o] 33 0.013474 0.0027754651 [oj 34 0.015372 0.0177618526 (oj 35 0.008561 0.0049488186 [oj 36 0.006391 0.0012198063 [o] 37 0.007973 0 0006500610 [oj 38 0.008006 0 0010331073 [o] 39 0.007628 0,0003803656 [oj 40 0.006568 0.0000318116 [oj 41 0.016317 0.0017054159 [oj 42 0.007885 0.0005298993 [o] 43 0.006263 0.0004679549 joj 44 0.022733 0.0004601932 [0] 45 0.010225 0.0028195812  92  46 0,006899 0.0006760735 47 0.011359 0.0018086425 48 0.009277 0.0008052467 49 0.015769 0.0017271583 [0] 50 0.016150 0.0047979774 [0] 51 0.009150 0.0080105886 [0] 52 0.019804 0.0004602058 [o] 53 0.006222 0.0000089103 0.0025070307 [0] 54 0,011405 0.0000007956 [0] 55 0,008038 0.0000483738 [0] 56 0.011899 0.0001368830 [0] 57 0.008605 0.0015173719 0.009826 58 [0] 59 0.006310 0.0001910912 [o] 60 0.006977 0.0008590907 [0] 61 0.006280 0.0004124309 [0] 62 0.013658 0.0035824336 to] 63 0.006280 0.0004409757 [o] 64 0.012536 0.0001394290 to] 65 0.006733 0.0044545904 [o] 66 0.006385 0.0065230867 W 67 0.020555 0.0068027927 [0] 68 0.009992 0.0000711405 [o] 69 0.009904 0.0000619128 [o] 70 0.026522 0.0083447937 [o] 71 0.016598 0.0016411453 to] 72 0.006352 0.0019203438 [0] 73 0.012911 0.0014810637 [0] 74 0,017114 0.0269276667 to] 75 0.021484 0.0000258306 [0] 76 0.015098 0.0023696346 to] 77 0.010147 0,0032982950 [0] 78 0.014795 0.0052510831 [o] 79 0.007297 0.0002989127 to] 80 0.013871 0.0010458955 to] 81 0.007411 0.0000785961 [0] 82 0.013918 0.0001293476 [0] 83 0.006461 0.0014437840 [0] 84 0.011009 0.0002028120 [o] 85 0.015050 0.0054818625 to] 86 0.009385 0,0002578690 to] 87 0.013517 0,0001609665 [o] 88 0.006398 0.0033163687 [o] 89 0.022472 0.0038846249 90 0.022520 0.0133653264 91 0.027128 0.0025183051 92 0.016122 0.0018106659 93 0.020877 0,0077889333 94 0.006312 0.0006634509 95 0.017879 0.0149916941 96 0.008260 0.0038020860 97 0.010962 0.0138879567 [oj 98 0.007350 00085223261 [0] 99 0.006225 0.0064958008 [0] 100 0.006955 0.0015455843 [o] 101 0.019647 0.0006889208 [o] 102 0.018701 0.0003617572 [o] 103 0.012231 00001092878 [o] 104 0.006248 0.0014125473 [0] 105 0.006226 0,0007755669 [o] 106 0.009969 0,0005964171 [o] 107 0.010351 0.0010607555 [o] 108 0.008847 0.0036933527 [o] 109 0.012234 0.0031444493 [o] 110 0.006390 0.0000004635 to] 111 0.008905 0.0002154496 [o] 112 0.008238 0.0011997753 [o] 113 0.010764 0.0379213169 [o] 114 0.011273 0.0004830722 [o] 115 0.006353 0.0003967240 [o] 116 0.017153 0,0069388337 [o] 117 0.011259 0.0058664731 [o] 118 0.019783 0.0074054413 [o] 119 0.019750 0.0001884097 [o] 120 0.017809 0.0089123892 [o] 121 0.018621 0.0082041239 [o] 122 0.023622 0.0123640178 [o] 123 0.013313 0.1928226203 [0] 124 0.008937 0.0000692309 [o] 125 0.006359 0.0002635980 jo] 126 0.007427 0.0189390127 W 127 0011918 0.0015554284 [o] 128 0.011273 0.0200481769 [o] 129 0.007058 0.0023538608 [0] 130 0.009374 0.0471574441 [o] 131 0.007654 0.0006722647 [0] 132 0.007208 0.0001392432 [o] 133 0.008610 0.0023372041 [0] 134 0.015863 0,0002407763 [0] 135 0.006451 0.0000708950 [o] 136 0.015745 0.0199013259 [0] 137 0.007747 0.0000806124 [0] 138 0.006232 0,0002460191  [o] [o] [o] [o]  M i c r o s c o p i c A c c i d e n t Prediction Models t o r Signalised Intersections  l°] 139 0.007026 [0] 140 0.006866 [0] 141 0.011916 [0] 142 0.011296 [0] 143 0.009648 to] 144 0.007201 [0] 145 0.015438 [0] 146 0.016296 [ol 147 0.017114 to] 148 0.009064 [0] 149 0.018942 [0] 150 0.006410 [0] 151 0.019915 to] 152 0.015177 [0] 153 0.006924 [0] 154 0.015013 [0] 155 0.006988 [0] 156 0.006921 [0] 157 0.011837 [0] 158 0.014165 [0] 159 0.010434 [o] 160 0.014094 lo] 161 0.024030 [0] 162 0.015776 [0] 163 0.007718 [o] 164 0.023207 [0] 165 0.007037 [o] 166 0.007522 [o] 167 0.006951 [0] 168 0.009816 [0] 169 0.028560 [0] 170 0.028580 [i] ? $Stop$  0.0039493907 0.0014397698 0.0085815061 0.0070819156 0.0007975730 0.0004957537 0.0145896049 0.0155632831 0.0223749205 0.0019125429 0.0132519715 0.0111316536 0.0050704558 0.0154527463 0.0005463689 0,0005292293 0.0042265719 0.0127476593 0.0194408149 0.0009196686 0.0056105438 0.0018650822 0.0371853895 0.0006301449 0.0000743885 0.0256439727 0.0104868412 0.0002143530 0.0090389494 0.0000098959 0.0181985386 0.0019639770  94  M i c r o s c o p i c A c c i d e n t P r e d i c t i o n Models for S i g n a l i s e d I n t e r s e c t i o n s  APPENDIX V STATISTICAL SIGNIFICANCE TESTS  95  VHcroseonie Accident Prediction Models I'or Signalised  Figure AV.1  Hhiwemttfts  Predicted Accidents vs. Pearson Residuals for AT 1  Predicted Accidents  Figure AV.2 Predicted Accidents vs. Average Squared Residuals and Estimated Variance for AT 1 250  !> 3  T J  200 4  rz 150 100  50 0  — 0  10  20  30  Predicted Accidents  96  Miersseonie Accident Prediction Models for Signalised Intersections  Figure AV.3  Predicted Accidents vs. Pearson Residuals for AT 5  Predicted Accidents  Figure AV.4 Predicted Accidents vs. Average Squared Residuals and Estimated Variance for AT 5  97  Figure AV.5  Predicted Accidents vs. Pearson Residuals for AT 7  Predicted Accidents  Figure AV.6 Predicted Accidents vs. Average Squared Residuals and Estimated Variance for AT 7  0.5  1  Predicted Accidents  it.  Figure AV.7  •  • --'.-t'rions  Predicted Accidents vs. Pearson Residuals for AT 13  Predicted Accidents  Figure AV.8 Predicted Accidents vs. Average Squared Residuals and Estimated Variance for AT 13  10  15  20  Predicted Accidents  99  M i c r o s * l i j i i c Accident Prediction Models  Figure AV.9  Predicted Accidents vs. Pearson Residuals for Total Accidents  3  2  <n |  *  •  *  •  •  *  9  1  S o ce G O  -1  i -2  ra o £ -3 -4  Predicted Accidents  Figure AV. 10 Predicted Accidents vs. Average Squared Residuals and Estimated Variance for Total Accidents  1800 1600 1400 1200 1000 800 600 400 200 0  10  20  30  40  50  60  70  80  90  100  Predicted Accidents  100  ,': :: .:  .  Figure AV.11 Accidents  .  i  Predicted Accidents vs. Pearson Residuals for Severe  _4 J Predicted Accidents  Figure AV. 12 Predicted Accidents vs. Average Squared Residuals and Estimated Variance for Severe Accidents  101  Figure AV. 13 Accidents  Predicted Accidents vs. Pearson Residuals for PDO  Predicted Accidents  Figure AV.14 Predicted Accidents vs. Average Squared Residuals and Estimated Variance for PDO Accidents  102  Y1icrisseo|dc .Accident Prediction Models for Signalised intersections  Figure AV. 15 Accidents  Predicted Accidents vs. Pearson Residuals for Left Turn  Predicted Accidents  Figure AV.16 Predicted Accidents vs. Average Squared Residuals and Estimated Variance for Left Turn Accidents  103  Microscopic Accident Prediction M  Predicted Accidents vs. Pearson Residuals for Right Turn  Figure AV.17 Accidents  2 •  1  i  o  ** «• *  e: -1  • »  c  j? -2  ra a:  o- -3  Predicted Accidents  Figure AV.18 Predicted Accidents vs. Average Squared Residuals and Estimated Variance for Right Turn Accidents  1  2  3  Predicted Accidents  104  

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