1. IDENTIFYING HIGH-RISK ROADWAYS THROUGH JERK-CLUSTER ANALYSIS Seyedeh-Maryam Mousavi, Louisiana State University Scott Parr, California State University - Fullerton Anurag Pande, California Polytechic State University Brian Wolshon, Louisiana State University 41st Annual International Forum on Traffic Records and Highway Information SystemsCosta Mesa, CA - November 2015 1

2. BACKGROUND  Research was initiated as part of a National Science Foundation study to examine stop-and-go, speed, and travel time parameters associated with the Two Fluid Model of traffic flow  GPS data collected to analyze those movements also showed evidence of “abrupt” and “abnormal” driving maneuvers  Based on these observations, it was suggested that there could be a correlation between the areas of “abrupt, atypical” movement and locations of crashes over time  In effect, the idea of this research was is that if we see a fatality in one of every 300 crashes and 1 crash occurs for every 10,000 abrupt maneuvers, we may be able to identify dangerous locations by looking first at locations with very high abrupt maneuvers  These locations would be much easier to identify because abrupt maneuvers occur much more frequently  This could suggest problem areas that could be corrected before crashes, injuries, and fatalities ever occur. Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 2

3. LITERATURE REVIEW State of practice for identifying high-risk road segments: using long-term historic traffic crash data However, crashes Would it be more effective to develop and apply surrogate measures of safety to identify high-risk roadway segments? Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 1.Are a reactive measure: They can only be used after damage, injury, and loss of life have occurred 2.Might occur due to several reasons 3.May not be recorded uniformly 3

4. WHAT IS A SURROGATE MEASURE OF SAFETY?  An observable, non-crash event that has a relationship with crashes enabling more powerful statistical analyses to be applied  Advantages : Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 4

5. RESEARCH HYPOTHESIS AND OBJECTIVE  Hypothesis Locating high concentrations of abnormal negative jerk values (jerk- clusters: rate of change of acceleration_ ft./s³) would enable high-risk locations to be identified in advance and potentially with greater accuracy.  Objective: Develop measures to proactively identify high-risk roadway segments. Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 5

6. METHODOLOGY Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 6 1. Data Collection GPS data collection and processing, road selection, ADT, curve location 2. Data Processing Data errors, linear referencing, jerk analysis, crash analysis 3. Sensitivity Analysis “Appropriate” jerk threshold and segment length 4. Crash Frequency Modeling and Log Likelihood ratio test Crash intensity v. jerk-clusters v. horizontal curvature, model fitness

7. 1. DATA COLLECTION : GPS DATA LOGGERS  GPS Data Loggers: Rechargeable battery; Recorded data in a comma-separated format; Collected: latitude, longitude, altitude, heading, speed, number of satellites utilized, universal time code (UTC) and date, and etc. Placed in the center console or glove box of the tested vehicles.  Readings were recorded at a rate of three hertz. Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 7

8. 1. DATA COLLECTION : PARTICIPANTS 1) To ensure the consistency of travel over the same routes for developing jerk-clusters: a questionnaire based on personal characteristics and driving patterns was developed; 2) Participants: 31 staff members and their household at Louisiana State University; 3) Participants were asked to refrain from allowing anyone else to use their vehicle; 4) To maintain confidentiality: a unique random identifier was assigned to each participant; 5) Data collection period: spanned from July 2012 to January 2013- With each driver contributing around 10 days. Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 8

9. Interrupted state highways in Baton Rouge, LA were selected in this study, based on the data frequency: 1)A 7.5 mile long stretch of LA 42, a four-lane divided highway with posted speeds varying from 45 to 55 mph; 2)A 5.15 mile long stretch of LA 1248, a four-lane divided highway with posted speeds varying from 30 to 45 mph. 1. DATA COLLECTION : ROADWAY SELECTION Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 9

10. 1)Average Daily Traffic (ADT) count data: accessed through LADOTD’s estimated annual average daily traffic counts 2)Roadway geometric features: drawn from Google maps. 1. DATA COLLECTION : ADT AND ROADWAY CURVATURE Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 10

11. 2. DATA PROCESSING: GPS DATA ERRORS GPS “noise” at intersections GPS position wandering GPS reading gaps Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 11

12. 2. DATA PROCESSING: GIS LINEAR REFERENCING  Linear referencing geographic locations (x, y) to a measured linear feature (LA 42 and LA 1248) within a radius of 300 ft. [14 and 16].  This was applied to the collected GPS data and crash data. Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 12

13. 2. DATA PROCESSING: JERK ANALYSIS Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 13

14. 2. DATA PROCESSING: SEGMENTING THE ROADWAYS Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement The roads were divided into three different segment lengths in ArcGIS: 1.Short: 1/8 mile segments 2.Medium: ¼ mile segments 3.Long: ½ mile segments 14

15. 2. DATA PROCESSING: CRASH RATE ANALYSIS Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 15

16. 3. SENSITIVITY ANALYSIS 1.21 jerk thresholds were evaluated, started at -0.5 ft. /sec3 and decreasing by 0.5 ft. /sec3 until reaching -10.5 ft. /sec3 2.Calculating “crash rate” and “jerk ratio” per each segment of 1/8, ¼, and ½ mile. 3.Implementing a sensitivity analysis for LA42 and LA1248 separately. Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 16

17. 3. SENSITIVITY ANALYSIS: LA42 Jerk Threshold: -2.5 ft./s3 Segment length: ¼ mile Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 17

18. 4. CRASH FREQUENCY MODELING & LOG LIKELIHOOD RATIO TEST Crash frequency model Independent variable: jerk ratio Log likelihood ratio test Crash frequency model Independent variables: jerk ratio & presence of horizontal curvature Dependent variable: Crash Ratio= Number of crashes per segment/ ADT Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 18

19. DATA PREPARATION BASED ON QUARTER MILE SEGMENTS 19

20. 4. CRASH FREQUENCY MODELING INDEPENDENT VARIABLE: JERK RATIO ParameterDFEstimateS.E. 95% Confidence Limits Chi-SquarePr > ChiSq Intercept1-1.34650.4644-2.2567-0.43638.410.0037 Jerk Ratio10.40600.08570.23800.573922.45<.0001 Dispersion10.28560.19300.07601.0736 ParameterDFEstimateS.E. 95% Confidence Limits Chi-SquarePr > ChiSq Intercept1-0.54480.5348-1.59300.50341.040.3084 Jerk Ratio10.17440.06720.04280.30616.750.0094 Dispersion10.15230.18290.01451.6025 Table 1- Crash Frequency Model LA42 Table 2- Crash Frequency Model LA1248 Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 20

21. 4. LIKELIHOOD RATIO TEST Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 21

22. 4. LIKELIHOOD RATIO TEST: INDEPENDENT VARIABLE: JERK RATIO RoadFull Log Likelihood LA42-48.4620 LA1248-35.8554 LA42 and LA1248-88.5092 The two models are significantly different. Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 22

23. ParameterDFEstimateS.E. 95% Confidence Limits Chi-SquarePr > ChiSq Intercept1-0.66150.5959-1.82940.50651.230.2670 Jerk Ratio10.17890.06740.04680.31107.040.0080 Curve10.10970.2431-0.36680.58620.200.6518 Dispersion10.14420.18160.01221.7020 ParameterDFEstimateS.E. 95% Confidence Limits Chi-SquarePr > ChiSq Intercept1-1.42790.4372-2.2847-0.571110.670.0011 Jerk Ratio10.46190.08220.30080.622931.60<.0001 Curve1-0.60950.2958-1.1893-0.02984.250.0393 Dispersion10.13740.17630.01111.6969 4. CRASH FREQUENCY MODELING INDEPENDENT VARIABLE: JERK RATIO & PRESENCE OF HORIZONTAL CURVATURE Table 1- Crash Frequency Model LA42 Table 2- Crash Frequency Model LA1248 Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 23

24. 4. LIKELIHOOD RATIO TEST: INDEPENDENT VARIABLE: JERK RATIO & PRESENCE OF HORIZONTAL CURVATURE The two models are significantly different. RoadFull Log Likelihood LA42-46.6056 LA1248-35.7548 LA42 and LA1248-88.0960 Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 24

25. COMPARING THE LOG LIKELIHOOD RATIO TESTS Comparing the full log likelihood values of 2 tables: Models including the presence of curvature performed better Generally: Inclusion of more variables improve models and offer better results. RoadFull Log Likelihood LA42-48.4620 LA1248-35.8554 LA42 and LA1248-88.5092 RoadFull Log Likelihood LA42-46.6056 LA1248-35.7548 LA42 and LA1248-88.0960 Independent Variable: Jerk RatioIndependent Variable: Jerk Ratio & Presence of Curvature Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 25

26. CONCLUSIONS  Strong correlation between the location of jerk-clusters and vehicle crash intensity on interrupted traffic flow routes LA42 and LA1248  This finding may permit identification of crash prone locations before crash data accumulates  Ideal segment length yields the highest correlation  Segments that were too short (less than or equal to 1/8 mile) or too large (greater than or equal to 1/2 mile) reduced the ability to correlate jerks and crashes Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 26

27. ACKNOWLEDGEMENTS The researchers gratefully acknowledge the financial support of the National Science Foundation through Grants #0927123 and #0927358, as well as the Gulf Coast Center for Evacuation and Transportation Resiliency, a United States Department of Transportation sponsored University Transportation Center and part of the Southwest Transportation University Transportation Center (SWUTC). Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement 27

28. QUESTIONS? Brian Wolshon Gulf Coast Research Center for Evacuation and Transportation Resiliency Louisiana State University Baton Rouge, LA 70803 brian@rsip.lsu.edu