Quantitative Safety Analysis for Intersections on Washington State Two-lane Rural Highways

1. Quantitative Safety Analysis for Intersections on Washington State Two-lane Rural Highways Master’s Thesis Defense Ngan Ha Nguyen 8/15/2007

2. Overview • Introduction • Study Routes and Data • Methodology • Data Analysis • Accident Risk Modeling • Conclusions and Recommendations

3. Leading Causes of U-I Deaths, U.S., 1969-2005 Average Comprehensive Cost by Injury Severity Introduction: Traffic Accidents • Traffic accidents are leading causes of death • Huge economic loss to the society • Improving traffic safety is an important task

4. Introduction: National Statistics • Rural fatal accident rate is more than twice as high as urban fatal accident rate

5. Introduction: National Statistics • More than 1 death per hour in accidents at intersections

6. Total annual VMT. Fatal and Disabling Accidents 25% 44% 56% 75% Two-lane rural highways Others Introduction: Washington State Stats • 4.5% increase in total accidents from 2004 to 2005

7. Introduction: Objective • Analyze causal factors of intersection accidents • Identify cost-effective solutions for intersection safety improvements

8. Overview • Introduction • Study Routes and Data • Methodology • Data Analysis • Accident Risk Modeling • Conclusions and Recommendations

9. Study Routes and Data: Collecting • Three sources: • Highway Safety Information System (HSIS) • WSDOT Office of Information Technology • WSDOT online tool, State Route Web (SRWeb) • Six years data ( 1999 -2004) • Roadway data • Accident data • Traffic data • Intersection data • 141 state routes

10. Study Routes and Data: preliminary steps • Focus on 3-legged and 4-legged intersections • Classify manually based on SRWeb. • Link intersection file to roadway files: • Roadway characteristic file, • Curvature file • Gradient file • Complicated process  not applicable for all 141 state routes  select six representative study routes

11. Study Routes and Data: six study routes • Two criteria • Route length • Geographic location and spatial alignment

12. Overview • Introduction • Study Routes and Data • Methodology • Data Analysis • Accident Risk Modeling • Conclusions and Recommendations

13. Decreasing approach Increasing approach Xs Xs Increasing milepost direction Methodology: Data Organization • Intersection approach section:

14. Methodology: Data Organization • Determining “intersection section” by using “Stopping Sight Distance” (SSD): • V = Approach speed, fps ( feet per second) • t = Perception/reaction time ( typically 1 sec) • d = Constant deceleration rate, fps2 (feet per second square) • t = 1 sec • d =10 ft/sec2

15. Methodology: Data Organization • Entity-Relationship (E/R) Diagram • Microsoft SQL Server are used to manage and query data

16. Methodology: Hypothesis testing • Test whether a variable has a significant impact on accident rate • T-test  testing variable has 2 groups • F-test (ANOVA)  testing variable has more than 2 groups

17. Methodology: Modeling • Nature of accident data: • Discrete • Non-negative • Randomly distribute • Poisson model • λi is the expected accident frequency • Xi is a vector of explanatory variables • β is a vector of estimable coefficient

18. Methodology: Modeling • Over-dispersion problem: mean not equal variance • Negative binomial model: • Over-dispersion parameter : select between Poisson model and negative binomial model • EXP(εi) is a gamma-distributed error term with mean 1 and variance α2

19. Methodology: Modeling • Parameters estimation using log-likelihood functions: • Poisson model • Negative binomial model • ni: number of accident happened during 6 consecutive study years • λi:expected accident frequency in 6 years • : over-dispersion parameter

20. Methodology: Modeling • Goodness of Fit: • The likelihood ratio test statistic is • Sum of model deviances • The ρ-statistic

21. Overview • Introduction • Study Routes and Data • Methodology • Data Analysis • Accident Risk Modeling • Conclusions and Recommendations

22. Data Analysis: Preliminary Analysis

23. Data Analysis: Statistical Analysis t-test

24. Data Analysis: Statistical Analysis t-test

25. Data Analysis: Statistical Analysis F-test

26. Data Analysis: Statistical Analysis F-test

27. Overview • Introduction • Study Routes and Data • Methodology • Data Analysis • Accident Risk Modeling • Conclusions and Recommendations

28. All-type Accident Risk Modeling • Negative binomial model applied • Over-dispersion parameter is significant • Model:

29. All-type Accident Risk Modeling • Result:

30. All-type Accident Risk Modeling • Goodness of fit:

31. Strike-At-Angle Accident Risk Modeling • Negative binomial model applied • Over-dispersion parameter is significant • Model:

32. Strike-At-Angle Accident Risk Modeling • Result:

33. Strike-At-Angle Accident Risk Modeling • Goodness of fit

34. Overview • Introduction • Data Processing • Methodology • Data Analysis • Accident Risk Modeling • Conclusions and Recommendations

35. Conclusions: • Reduce speed limit at the intersection • Put more signage ahead of the intersections • Increase shoulder width (greater than 6 feet) around the intersection area • Keep the shoulder width consistent along the intersection sections • Decrease the degree of curvature at the intersection locations • Decrease the slopes (less than 5%) along the intersection area

36. Recommendations • Negative binomial model is chosen over Poisson model for modeling accident frequency • Before-and-after studies on safety at intersections that have traffic control device or feature illumination installed are needed • More data: • Crossing roads • Human activity • Detailed intersection layout

37. Ngan Ha Nguyen nganhanguyen@gmail.com