Development of Accident Prediction Models for the Highways of Thailand Lalita Thakali Transportation Engineering

Outline of Presentation  Statements of Problem  Objective  Methodology  Preliminary analysis  Model development  Identification of hazardous location  Conclusion  Recommendation

Statement of Problems  18%  18% of the annual road accidents occurs in highway of Thailand.  Trend of accident in highways of Thailand  Budget allocation for road safety for highways of Thailand YearAccidentsFatalitiesInjuries Property Damage (THB) ,3412,21212,712352,851, ,0662,26513,285445,236, ,1712,02312,984464,248, ,5472,32418,381425,623, ,2872,16915,300405,248,000 YearBudget (in Million Baht) 20021, , , , (The annual report (2005) of the Bureau of Traffic Safety ) 2.13%  In 2002 the economic losses due to road accidents was estimated to be in approximately million baht, or 2.13% of the GDP

Causes of Accidents in Thailand 63% 1% 3% 6% 20% 5% Road & Environment Vehicle Human 4% Descriptive model 1 Predictive model 2 Risk model 3 Accident consequences model 4 How to Address Road Safety Problem By Accident Modeling

Objective of Study accident characteristic Identify existing accident characteristic generalized accident prediction models To develop a generalized accident prediction models for highways using different statistical techniques. hazardous locations To identify hazardous locations 1 2 3

Methodology Literatures Review Site Selection Data Collection DoH historical Accident Data DoH traffic data Metrological data Video data Explanatory Variables (Xij) Homogenous Section i= 1,2….n i= 1,2….n l1 l1 l2 l2 ln-1 ln-1 ln ln Monthly accident data (λij) Accident Fatality Injury Property damage Identification of possible Variables Preliminary data analysis (Characteristic of accident & severities

Site Selection 1. Why route no 4 Year Accidents (highways) Accidents (major route 1,2,3,4) Accidents (route 4) % of accidents (major route) % of accidents (route 4 w.r.t. major routes) Average Why route no 4 in Ratcha Buri & Nakhonpathom YearAccident Fatalit y Injury Property Damage Average high no of AADT count stations 3. Relatively high no of AADT count stations 27.59% 8.56% 27.59% of accident occurs in selected site, where it covers only 8.56% of Total road length of route no 4

Study Area Nakhonpathom Ratcha Buri Total length = km Route no 4

Methodology Literatures Review Site Selection Data Collection DoH historical Accident Data DoH traffic data Metrological data Video data Explanatory Variables (Xij) Homogenous Section i= 1,2….n i= 1,2….n l1 l1 l2 l2 ln-1 ln-1 ln ln Monthly accident data (λij) Accident Fatality Injury Property damage Identification of possible Variables Preliminary data analysis (Characteristic of accident & severities

Data Collection 1.DoH historical Accident Data 2.DoH traffic data 3.Metrological data 4.Video data 1.DoH historical Accident Data 2.DoH traffic data 3.Metrological data 4.Video data Calendar  No of lane (4,1))  Types of median (4,1)  Shoulders available (4)  No of curves (4)  No of intersection (4)  No of access (4)  AADT (2)  % of heavy vehicle (2)  Rainfall (3)  Month Geometric Traffic Weather Accident  Total accident  Fatality  Injury  Property damage DOH (1) Data: Year 2001 to

Methodology Literatures Review Site Selection Data Collection DoH historical Accident Data DoH traffic data Metrological data Video data Explanatory Variables (Xij) Homogenous Section i= 1,2….n i= 1,2….n l1 l1 l2 l2 ln-1 ln-1 ln ln Monthly accident data (λij) Accident Fatality Injury Property damage Identification of possible Variables Preliminary data analysis (Characteristic of accident & severities

Accident Rate (MVK) Countries CanadaFranceGermanyItalyUKUSABahrainEgyptOmanYemen seven Average Fatality rate in this study area is much higher than the rate in other countries. seven times greater than that of Egypt Fatality rate per MVK Objective1

Causes of Accident Location of Accident Accident Fatality Injury Property damage S.NTypes of Causes Total % 1Maximum speed limit Maximum speed limit + others Improper passing Improper passing +others Failure to yield right Failure to temporary stop, slow down, turn Disregarding traffic signal marking Vehicle defective Drunkeness ASleepy BOthers Total Objective1 The exceeding of max speed is mostly due to the human- vehicle and its interaction with the geometric features of the road- this could be addressed in the model with the inclusion of geometric variables

Weather related Accident Vehicle involvement VehiclesTotal% Pedestrian Bicycle30.15 Tricycle20.10 Motorcycle Trimotcycle Passenger car Light bus Light truck Heavy vehicle (HV) Heavy bus Medium truck10.05 Heavy truck Farm vehicle WeatherAccident%Fatality%Injury%PD% Clear Fog Rain Other Total Surface Condition Accident %Fatality%Injury%PD% Dry Dirty Wet Other Total Note: PD= Property Damage (1000 baht) Objective1 HV only 16% of total number of accidents while it represents 22.39% of total traffic volume

Accident distribution based on month Objective1

Methodology Literatures Review Site Selection Data Collection DoH historical Accident Data DoH traffic data Metrological data Video data Explanatory Variables (Xij) Homogenous Section i= 1,2….n i= 1,2….n l1 l1 l2 l2 ln-1 ln-1 ln ln Monthly accident data (λij) AccidentAccident FatalityFatality InjuryInjury Property damageProperty damage Identification of possible Variables Preliminary data analysis (Characteristic of accident & severities

VariablesUnits AADTNumber (*1000) HV% LaneNumber Lengthkm Access (A’)Number/km Intersection(I’)Number/km Curve (C’)Number/km Rain (R)mm VariablesCategory Median (MD) Divided (1) Undivided (0) Shoulder (S) No (1) Yes (0) Month (M) Others (1) April (0) Addition of variables Forward selection Literatures Preliminary analysis Data availability VariablesTotalMeanStdUnits Accident Number Fatality Person Injury Person PD Thousand baht Variables (per month) Dependent Independent AADT and lane is highly correlated, so lane has been excluded Objective 2 Data from was used in model development

VariablesAccidentFatalityInjuryPD AADT (*1000) HV Length (km) LaneAICR Dependent Variables Accident 1 Fatality Injury PD Independent Variables AADT HV Length Lane A I C R Pearson Correlation

VariablesAccidentFatalityInjuryPD AADT (*1000) HV (%) Length (km) A'I'C'R Dependent Variables Accident 1 Fatality Injury PD Independent Variables AADT HV Length A' I' C' R Pearson Correlation

Methodology cont. Forward selection of variables Model development GLM- Poisson regression GLM- NB regression E(λ) = exp∑βjXij λ = accident per month βj= parameter coefficient Xi= explanatory variable Is included variable significant? And is the goodness of fit better? Identification of hazardous location Accident Data 2006 (Visual validation) Selection of model (Poisson or NB) If yes Continue to include If not Exclude the variable Any explanatory variables remaining? Yes No

Generalized Linear Model ? Risk model Empirical Bayes Accident Modeling Descriptive model Accident consequences Model Predictivemodel Multivariate Fuzzy Logic Artificial Neural Network Linear Model GLM Normal dis of accident with constant mean & variance Only Poisson /Negative Binomial regression model- poisson trial Accident- Normally follows poisson trial rather than binomial trial Objective 2

E(λ) =μ= ∑ βiXi Linear model η= ∑βiXi Generalized Linear Model λ (i, t) = e ∑βjXij  Link function used gives non negative value which comply with nature of accident.  Parameter is estimated by max likelihood method unlike OLS method. Generalized Linear Model cont. Objective 2 Link Function

Discuss about the goodness of fit Significance of parameters Estimation of parameters β Maximum log likelihood method. SPSS (16) 95% confident interval = standard deviation W = Wald value TestsFormulaCriteriaPurpose Log likelihood (LR) test. P value >0.05To select Step Deviance Less the value better is the selected step model “ AIC““ BIC““ Total explained variation (R 2 D ) Greater the value better is the model To select either Poisson or NB Objective 2 Significance & Goodness of tests

Variables /goodness of fit Steps Constant (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -851 (0.000) -739 (0.000) (0.000) (0.000) AADT (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Length (0.000) (0.000) (0.000) 0.08 (0.000) (0.000) (0.00) (0.00) (0.000) Access (0.000) (0.000) (0.000) 0.101(0.0 00) (0.000) HV (0.000) (0.000) (0.000) (0.000) (0.004) Median (0.083) Shoulder (0.000) (0.000) (0.000) Month (0.000) (0.000) Intersection (0.380) Curve (0.020) (0.020) Rain0.0 (0.059) Deviance Pearson-Chi LL AIC BIC LR ratio 0 (0.000) 562 (0.000) 670 (0.000) 790 (0.000) 806 (0.000) 810 (0.000) 865 (0.000) 889 (0.000) 890 (0.000) 899 (0.000) 903 (0.000) R2 DR2 D Detail forward selection procedure for Accident model (Poisson)

Variables/Goodness Tests AccidentFatalityInjuryProperty Damage (PD)*1000 baht Poisson (1) Negative Binomial (2) Poisson (3) Negative Binomial (4) Poisson (5) Negative Binomial (6) Poisson (7) Negative Binomial (8) Selected Step Constant (0.000) (0.027) (0.000) (0.000) (0.00) (0.000) (0.00)2.100 (0.000) AADT (1000)0.015 (0.000)0.014 (0.000)0.016 (0.000)0.017 (0.00)0.015 (0.000)0.013 (0.00)0.018 (0.00)0.031 (0.00) length (km)0.112 (0.00)0.121 (0.000)0.102 (0.002)0.093 (0.003)0.148 (0.000)0.155 (0.00)0.087 (0.00) (0.055) Access (per km)0.103 (0.000)0.115 (0.000)0.097 (0.017)0.104 (029)0.210 (0.000)0.224 (0.00) (0.00)-0.21 (0.000) HV (%) (0.004) (0.053) (0.00) Median (0.030) (0.008) (0.003) (0.004) (0.00) Shoulder0.769 (0.000)0.884 (0.00)0.963 (0.030)0.791 (0.042)0.561 (0.001) (0.0017) (0.00)1.086 (0.00) Month (0.000) (0.001) (0.000) (0.011) (0.000) (0.00) (0.00) Intersection (per km) (0.00) (0.00) Curve (per km)0.298 (0.020)0.333 (0.013) 0.43 (0.005)0.381 (0.037) (0.00)-0.33 (0.004) Rain (mm) (0.012) (0.016) (0.000) (0.00) Deviance Scaled Deviance Pearson Chi-Square Scaled Pearson LL AIC BIC LR ratio 899 (0.000) 386 (0.000) 72 (0.000) 66 (0.000) 726 (0.000) 410 (0.000) (0.000) 1350 (0.000) R2DR2D

Prediction Models Accident Fatality Injury Property Damage Objective 2 Unit: per month

Multiplier Factors Objective 2 Annual Average Daily Traffic Length The factor is computed for its changes in magnitude of each predicting variables while considering all the other variables to be constant Percent of heavy vehicle No of Access per km

Objective 2 Median Shoulder Multiplier factors cont. Intersection

Objective 2 No of Curve per km Rain fall Multiplier factors cont.

Methodology cont. Forward selection of variables Model development GLM- Poisson regression GLM- NB regression E(λ) = exp∑βjXij λ = accident per month βj= parameter coefficient Xi= explanatory variable Is included variable significant? And is the goodness of fit better? Identification of hazardous location Accident Data 2006Accident Data 2006 (Visual validation)(Visual validation) Selection of model (Poisson or NB) If yes Continue to include If not Exclude the variable Any explanatory variables remaining? Yes No

Comparative study : Actual vs Model prediction Predicting Variables MeanStandard DeviationCritical Frequency ActualModelActualModelActualModel Accident Fatality Injury PD Predicting Variables MeanStandard DeviationCritical Rate ActualModelActualModelActualModel Accident Fatality Injury PD Visual validation  Total road section of km  Road section divided into constant length of 2km, with few less then 2 km.

Control Section Chainage AADT Length (km) A’HVMDSI’C’ FromTo No No Yes Yes Yes Yes Yes Yes No No No No No Yes Control Section Month Chainage AADT Length (km) Hazardous location FromTo FrequencyRate Jan- Dec Yes Jan- Dec Yes April Yes Objective 3 Hazardous Locations for accident

Objective 3Nakhonpathom Jan- Dec April

 Accident trend is highly dependent on the exposure factors (MVK).  76% of accidents - exceeding of speed limit.  Light vehicles have comparatively greater influence to the accidents than the HV.  April has higher trend of accident and its severity than in rest of the months. S.NVariables Total Explained variation (%) PoissonNegative Binomial 1Accident3934 2Fatality1819 3Injury3332 4Property Damage3929 AADT (1,2,3,4) Length (1,2,3,4) Access per km (1,2,3,4) HV % (1,4) Median (2,3) Shoulder (1,2,3,4 Month (1,2,4) Intersection per km (4) Curve per km (1,3,4) Rainfall (2,3) Model Development Characteristic of Accidents Conclusions Significant variables

Total explanatory variation is not surprising as data excludes detail station of traffic count, detail geometric data like lane width, shoulder width and the human behaviors. Comparable with to Caliendo et al. (2007). The variables on the different severity of accident comply with the preliminary analysis. i.e. methodology implemented for the model formulation is appropriate one. Conclusions cont. Identification of hazardous location Using the accident prediction models as the tool for the identification of hazardous section, the road sections with high traffic volume, high number of curves per km and absence of shoulder were found to be hazardous.

Recommendations  From preliminary analysis & the models accident is prominent in April. Hence, more instant safety measures would be taken to reduce the numbers of accidents during this period which would safe both huge life and economic losses.  Accident is enhanced by the light vehicles as depicted in the result. Traffic management enforcing the rules and regulation would be implemented such as provision of separate lanes.  The developed accident prediction models would be integrated with the GIS tools and develop interface that would explicitly present the hazardous road sections. Future Researches  Develop separate accident prediction models for intersection.  Develop model with inclusion of more detail geometric data like width of lane, width shoulder, speed limit etc.

Recommendations cont.  Separate accident prediction model, such as for vehicle to vehicle collision, vehicle turn over etc.  Real time crash prediction model would be developed for the link and intersection provided the data availability is real time.  The real time crash prediction would be integrated with the simulation package in the network for the traffic assignment with the safety factor with addition to the delay factors.  The real time traffic model would be integrated with GIS or Google earth to display the risk of particular section.