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

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

3. 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) 200115,3412,21212,712352,851,000 200215,0662,26513,285445,236,000 200315,1712,02312,984464,248,000 200418,5472,32418,381425,623,000 200516,2872,16915,300405,248,000 YearBudget (in Million Baht) 20021,400.000 20031,400.000 20041,770.000 20051,644.999 (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 115932 million baht, or 2.13% of the GDP

4. 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

5. 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

6. 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

7. 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) 200115341322880021.0424.783 200215066314286920.8527.658 200315171298294919.6631.824 200418547353499319.0528.098 200516287301686118.5228.548 200610597207755219.6026.577 Average15168299783719.7927.91 2. Why route no 4 in Ratcha Buri & Nakhonpathom YearAccident Fatalit y Injury Property Damage 200129.915.110.415.6 200223.510.08.613.8 200319.05.05.27.3 200434.411.424.010.3 200529.610.014.97.3 200629.23.022.35.9 Average27.599.1014.2410.04 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

8. Study Area Nakhonpathom Ratcha Buri Total length = 117.93 km Route no 4

9. 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

10. 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 2006 2001- 2006

11. 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

12. Accident Rate (MVK) Countries CanadaFranceGermanyItalyUKUSABahrainEgyptOmanYemen 0.010.02 0.01 0.0010.0020.440.040.11. 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 - 3.08 per MVK Objective1

13. Causes of Accident Location of Accident Accident Fatality Injury Property damage S.NTypes of Causes Total % 1Maximum speed limit 1051 76.10 2Maximum speed limit + others 31 2.24 3Improper passing 47 3.40 4Improper passing +others 4 0.29 5Failure to yield right 1 0.07 6 Failure to temporary stop, slow down, turn 1 0.07 7 Disregarding traffic signal marking 7 0.51 8Vehicle defective 24 1.74 9Drunkeness 1 0.07 ASleepy 8 0.58 BOthers 206 14.92 Total 1381 100 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

14. Weather related Accident Vehicle involvement VehiclesTotal% Pedestrian211.04 Bicycle30.15 Tricycle20.10 Motorcycle39219.34 Trimotcycle48023.68 Passenger car60529.85 Light bus592.91 Light truck984.83 Heavy vehicle (HV) Heavy bus21810.75 Medium truck10.05 Heavy truck924.54 Farm vehicle562.76 WeatherAccident%Fatality%Injury%PD% Clear1018745992421651429587 Fog100000220 Rain125900406165410 Other2371758187293982 Total13811006410064810016369100 Surface Condition Accident %Fatality%Injury%PD% Dry 990725992394611404586 Dirty 100000.00270 Wet 125900407162810 Other 2651958214336694 Total 13811006410064810016369100 Note: PD= Property Damage (1000 baht) Objective1 HV only 16% of total number of accidents while it represents 22.39% of total traffic volume

15. Accident distribution based on month Objective1

16. 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

17. 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 Accident12201.221.89Number Fatality610.060.33Person Injury5780.541.68Person PD1559615.6650.05 Thousand baht Variables (per month) Dependent Independent AADT and lane is highly correlated, so lane has been excluded Objective 2 Data from 2001- 2005 was used in model development

18. VariablesAccidentFatalityInjuryPD AADT (*1000) HV Length (km) LaneAICR Dependent Variables Accident 1 Fatality 0.2171 Injury 0.650.2511 PD 0.3860.1890.2011 Independent Variables AADT 0.4880.1320.2580.2951 HV 0.0830.0060.0710.1180.3351 Length 0.1490.0350.109 - 0.005 -0.148-0.071 Lane 0.4610.1170.2380.3630.8990.379-0.2941 A 0.5360.1450.4870.0750.276-0.050.540.2291 I 0.0190.028-0.0480.014-0.126 - 0.172 0.587 - 0.247 0.3671 C 0.141-0.0120.083 - 0.128 0.113 - 0.084 0.249 - 0.067 0.3570.7241 R -0.025-0.078-0.0770.0020.052 - 0.004 -0.0230.053 - 0.002 - 0.019 -0.021 Pearson Correlation

19. VariablesAccidentFatalityInjuryPD AADT (*1000) HV (%) Length (km) A'I'C'R Dependent Variables Accident 1 Fatality 0.2171 Injury 0.6500.2511 PD 0.3860.1890.2011 Independent Variables AADT 0.4870.1320.2580.2951 HV 0.0900.0080.0740.1220.3411 Length 0.1490.0350.109-0.005-0.148-0.0661 A'0.3820.1230.3750.0430.3870.222-0.2431 I'-0.196-0.031-0.131-0.097-0.183-0.276-0.4020.1001 C'-0.074-0.023-0.051-0.156-0.158-0.442-0.229-0.1680.6991 R -0.025-0.078-0.0770.0020.052-0.005-0.0230.021-0.002-0.0241 Pearson Correlation

20. 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

21. 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

22. 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

23. 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

24. Variables /goodness of fit Steps 1234567891011 Constant 0.203 (0.000) - 0.656 (0.000) - 1.13 (0.000) -1.426 (0.000) -1.019 (0.000) -1.147 (0.000) -1.132 (0.000) -851 (0.000) -739 (0.000) -1.235 (0.000) -1.189 (0.000) AADT 0.018 (0.000) 0.019 (0.000) 0.016 (0.000) 0.017 (0.000) 0.016 (0.000) 0.015 (0.000) Length 0.063 (0.000) 0.078 (0.000) 0.079 (0.000) 0.08 (0.000) 0.101 (0.000) 0.097 (0.00) 0.112 (0.00) 0.112 (0.000) Access 0.118 (0.000) 0.125 (0.000) 0.126 (0.000) 0.101(0.0 00) 0.103 (0.000) HV -0.021 (0.000) -0.025 (0.000) -0.024 (0.000) -0.025 (0.000) -0.017 (0.004) Median 0.233 (0.083) Shoulder 0.639 (0.000) 0.601 (0.000) 0.769 (0.000) Month -0.348 (0.000) -0.338 (0.000) Intersection -0.055 (0.380) Curve 0.298 (0.020) 0.294 (0.020) Rain0.0 (0.059) Deviance22811719161014911474147114151392139113821378 Pearson-Chi28861866172415611531152915331490 14811479 LL-1796-1515-1460-1401-1392-1391-1363-1351 -1346-1344 AIC35933034292728092795279427382716271727082707 BIC35983044294228292819282327672750275627482751 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 0.250.290.35 0.380.39 Detail forward selection procedure for Accident model (Poisson)

25. 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 10 11 10 Constant -1.235 (0.000) -1.162 (0.027) -2.855 (0.000) - 2.576 (0.000) -2.153 (0.00) -1.911 (0.000) -0.449 (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)-0.056 (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)-0.115 (0.00)-0.21 (0.000) HV (%) -0.017 (0.004) - 0.017 (0.053) -0.009 (0.00) Median -1.054 (0.030) - 1.217 (0.008) -0.506 (0.003) -0.654 (0.004) 1.783 (0.00) Shoulder0.769 (0.000)0.884 (0.00)0.963 (0.030)0.791 (0.042)0.561 (0.001) 0.767 (0.0017) 1.567 (0.00)1.086 (0.00) Month -0.348 (0.000) - 0.552 (0.001) -0.916 (0.000) - 0.906 (0.011) -0.714 (0.000) -0.814 (0.00)-0.206 (0.00) Intersection (per km) 0.589 (0.00)-0.342 (0.00) Curve (per km)0.298 (0.020)0.333 (0.013) 0.43 (0.005)0.381 (0.037)-1.427 (0.00)-0.33 (0.004) Rain (mm) -0.003 (0.012) - 0.005 (0.016) -0.002 (0.000) -0.003 (0.00) Deviance13827393292671459841367303182 Scaled Deviance13827393292671459841367303182 Pearson Chi-Square14817511567148034942459963009800 Scaled Pearson14817511567148034942459963009800 LL-1346-1332-211-200-980-793-19300-3092 AIC2708268044141719781604386206198 BIC2748271948545620231648386706232 LR ratio 899 (0.000) 386 (0.000) 72 (0.000) 66 (0.000) 726 (0.000) 410 (0.000) 23509 (0.000) 1350 (0.000) R2DR2D 0.390.340.180.190.330.320.390.29

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

27. 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

28. Objective 2 Median Shoulder Multiplier factors cont. Intersection

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

30. 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

31. Comparative study : Actual vs Model prediction Predicting Variables MeanStandard DeviationCritical Frequency ActualModelActualModelActualModel Accident0.421.650.761.201.182.85 Fatality0.010.110.070.120.070.23 Injury0.330.940.741.321.062.26 PD1.8322.797.2023.139.0345.92 Predicting Variables MeanStandard DeviationCritical Rate ActualModelActualModelActualModel Accident3.5113.636.4810.219.9923.84 Fatality0.070.940.541.010.621.95 Injury2.787.866.2411.339.0219.19 PD14.77183.3857.08182.1571.85365.52 Visual validation  Total road section of 26.98 km  Road section divided into constant length of 2km, with few less then 2 km.

32. Control Section Chainage AADT Length (km) A’HVMDSI’C’ FromTo 201.1.126+42027+700117.1871.2812.536.11No00 201.1.227+70029+700117.18721236.11No10.5 201.2.129+70031+700117.1872536.11Yes11 201.2.231+70033+700117.1872436.11Yes0.5 201.2.333+70035+700117.18723.536.11Yes0.51 201.2.435+70037+700117.18722.536.11Yes10.5 201.2.537+70039+700117.1872336.11Yes0.5 201.2.639+70041+700117.1872336.11Yes00 202.1.141+700 43+830 126.0682.133.2825.141No1.4061 202.1.2 43+83045+830 126.06822.525.141No10.5 202.1.3 45+83047+830 126.0682225.141No20.5 202.1.4 47+83049+830 126.0682325.141No10.5 202.1.5 49+83051+830 126.0682225.141No0.5 202.2.1 51+83053+830 126.0681.571.2825.141Yes1.2760.319 Control Section Month Chainage AADT Length (km) Hazardous location FromTo FrequencyRate 201.1.1Jan- Dec26+42027+700117.21.28Yes 201.1.2Jan- Dec27+70029+700117.22Yes 202.1.1April41+70043+830126.12.13Yes Objective 3 Hazardous Locations for accident

33. Objective 3Nakhonpathom Jan- Dec April

34.  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

35. 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.

36. 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.

37. 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.