Adopting Machine Learning Methods to Predict Red-light Running Violations

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Volumn 2015-October, Issue , 2015, Pages 650-655

  Jahangiri, Arash ^a   Rakha, Hesham A ^b   Dingus, Thomas A ^a  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^b Virginia Polytechnic Institute and State University   (United States)

Author keywords

Driver violation; Machine learning; Random forest; Red light running; Signalized intersection; Support vector machine; Violation prediction

Indexed keywords

ARTIFICIAL INTELLIGENCE; CRASHWORTHINESS; DECISION TREES; HIGHWAY TRAFFIC CONTROL; INFORMATION USE; INTELLIGENT SYSTEMS; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; LEARNING SYSTEMS; SUPPORT VECTOR MACHINES; TRAFFIC SIGNALS; TRANSPORTATION; VEHICLES; VIDEO CAMERAS;

DRIVER VIOLATION; FEATURE SELECTION METHODS; MACHINE LEARNING TECHNIQUES; MINIMUM REDUNDANCY-MAXIMUM RELEVANCES; RANDOM FORESTS; RED LIGHT RUNNING VIOLATION; RED-LIGHT RUNNING; SIGNALIZED INTERSECTION;

FORECASTING;

EID: 84950265747     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ITSC.2015.112     Document Type: Conference Paper

References (50)

1. National Highway Traffic Safety Administration, Traffic safety facts 2012. 2014, National Center for Statistics and Analysis, US Department of Transportation, Washington, DC.
2. Insurance Institute for Highway Safety (IIHS). Red light running. Available from: http: //www. iihs.org/iihs/topics/t/red-lightrunning/topicoverview.
3. Gazis, D., R. Herman, and A. Maradudin, The problem of the amber signal light in traffic flow. Operations Research, 1960. 8 (1): p. 112-132.
4. Sheffi, Y. and H. Mahmassani, A model of driver behavior at high speed signalized intersections. Transportation Science, 1981. 15 (1): p. 50-61.
5. Bonneson, J. A., et al., Intelligent detection-control system for rural signalized intersections. 2002, Texas Transportation Institute, Texas A&M University System.
6. Gates, T. J., et al., Analysis of driver behavior in dilemma zones at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board, 2007. 2030 (1): p. 29-39.
7. Rakha, H., I. El-Shawarby, and J. R. Setti, Characterizing driver behavior on signalized intersection approaches at the onset of a yellow-phase trigger. Intelligent Transportation Systems, IEEE Transactions on, 2007. 8 (4): p. 630-640.
8. Pant, P. D., et al., Field testing and implementation of dilemma zone protection and signal coordination at closely-spaced high-speed intersections. 2005, University of Cincinnati.
9. Chang, M.-S., C. J. Messer, and A. J. Santiago, Timing traffic signal change intervals based on driver behavior. 1985.
10. Zegeer, C. V., GREEN-EXTENSION SYSTEMS AT EHGI-I-SPEED INTERSECTIONS. 1978.
11. Liu, Y., et al., Empirical observations of dynamic dilemma zones at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board, 2007. 2035 (1): p. 122-133.
12. Wei, H., et al., Quantifying Dynamic Factors Contributing to Dilemma Zone at High-Speed Signalized Intersections. Transportation Research Record: Journal of the Transportation Research Board, 2011. 2259 (1): p. 202-212.
13. Ghanipoor Machiani, S. and M. Abbas. Dynamic Driver's Perception of Dilemma Zone: Experimental Design and Analysis of Driver's Learning in a Simulator Study. in The 93nd Annual Meeting of the Transportation Research Board. 2014. Washington, DC.
14. Caird, J. K., et al., The effect of yellow light onset time on older and younger drivers' perception response time (PRT) and intersection behavior. Transportation research part F: traffic psychology and behaviour, 2007. 10 (5): p. 383-396.
15. El-Shawarby, I., et al. Age and gender impact on driver behavior at the onset of a yellow phase on high-speed signalized intersection approaches. in Transportation Research Board 86th Annual Meeting. 2007.
16. Li, H., H. Rakha, and I. El-Shawarby, Designing Yellow Intervals for Rainy and Wet Roadway Conditions. International Journal of Transportation Science and Technology, 2012. 1 (2): p. 171-190.
17. Mussa, R. N., et al., Simulator evaluation of green and flashing amber signal phasing. Transportation Research Record: Journal of the Transportation Research Board, 1996. 1550 (1): p. 23-29.
18. Amer, A., H. Rakha, and I. El-Shawarby, Agent-based stochastic modeling of driver decision at onset of yellow light at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board, 2011. 2241 (1): p. 68-77.
19. Jahangiri, A., H. Rakha, and T. A. Dingus, Predicting Red-light Running Violations at Signalized Intersections Using Machine Learning Techniques. 2015.
20. Porter, B. E. and K. J. England, Predicting red-light running behavior: a traffic safety study in three urban settings. Journal of Safety Research, 2000. 31 (1): p. 1-8.
21. Retting, R. A. and A. F. Williams, Characteristics of red light violators: results of a field investigation. Journal of Safety Research, 1996. 27 (1): p. 9-15.
22. Porter, B. E. and T. D. Berry, A nationwide survey of self-reported red light running: measuring prevalence, predictors, and perceived consequences. Accident Analysis & Prevention, 2001. 33 (6): p. 735-741.
23. Bonneson, J. A. and H. J. Son, Prediction of expected red-light-running frequency at urban intersections. Transportation Research Record: Journal of the Transportation Research Board, 2003. 1830 (1): p. 38-47.
24. Neale, V. L. and C. C. McGhee, Intersection decision support: evaluation of a violation Warning system to mitigate straight crossing path collisions. 2006, Virginia Transportation Research Council.
25. Retting, R. A., S. A. Ferguson, and C. M. Farmer, Reducing red light running through longer yellow signal timing and red light camera enforcement: results of a field investigation. Accident Analysis & Prevention, 2008. 40 (1): p. 327-333.
26. Elhenawy, M., H. Rakha, and I. El-Shawarby. Enhancing Driver Stop/Run Modeling at the Onset of a Yellow Indication using Historical Behavior and Machine Learning Techniques. in Transportation Research Board 93rd Annual Meeting. 2014.
27. Ghanipoor Machiani, S. and M. Abbas. Predicting Drivers Decision in Dilemma Zone in a Driving Simulator Environment using Canonical Discriminant Analysis. in The 93nd Annual Meeting of the Transportation Research Board. 2014. Washington, DC.
28. Liang, Y., M. L. Reyes, and J. D. Lee, Real-time detection of driver cognitive distraction using support vector machines. Intelligent Transportation Systems, IEEE Transactions on, 2007. 8 (2): p. 340-350.
29. Kim, Z., Robust lane detection and tracking in challenging scenarios. Intelligent Transportation Systems, IEEE Transactions on, 2008. 9 (1): p. 16-26.
30. Jahangiri, A. and H. Rakha. Developing a Support Vector Machine (SVM) Classifier for Transportation Mode Identification by Using Mobile Phone Sensor Data. in Transportation Research Board 93rd Annual Meeting. 2014.
31. Jahangiri, A. and H. A. Rakha, Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data. Intelligent Transportation Systems, IEEE Transactions on, 2015. PP (99): p. 1-12.
32. Balali, V. and M. Golparvar-Fard. Video-Based Detection and Classification of US Traffic Signs and Mile Markers using Color Candidate Extraction and Feature-Based Recognition. in Computing in Civil and Building Engineering (2014). ASCE.
33. Yuan, F. and R. L. Cheu, Incident detection using support vector machines. Transportation Research Part C: Emerging Technologies, 2003. 11 (3): p. 309-328.
34. Aoude, G. S., et al., Driver behavior classification at intersections and validation on large naturalistic data set. Intelligent Transportation Systems, IEEE Transactions on, 2012. 13 (2): p. 724-736.
35. Elmitiny, N., et al., Classification analysis of driver's stop/go decision and red-light running violation. Accident Analysis & Prevention, 2010. 42 (1): p. 101-111.
36. Zhang, L., et al., Prediction of red light running based on statistics of discrete point sensors. Transportation Research Record: Journal of the Transportation Research Board, 2009. 2128 (1): p. 132-142.
37. Doerzaph, Z. R. and V. Neale. Data acquisition method for developing crash avoidance algorithms through innovative roadside data collection. in Transportation Research Board 89th Annual Meeting. 2010.
38. Breiman, L., Random forests. Machine learning, 2001. 45 (1): p. 5-32.
39. Hastie, T., et al., The elements of statistical learning. Vol. 2. 2009: Springer.
40. Hsu, C.-W. and C.-J. Lin, A comparison of methods for multiclass support vector machines. Neural Networks, IEEE Transactions on, 2002. 13 (2): p. 415-425.
41. Hsu, C.-W., C.-C. Chang, and C.-J. Lin, A practical guide to support vector classification. 2003.
42. McLaughlin, S. B., J. M. Hankey, and T. A. Dingus, A method for evaluating collision avoidance systems using naturalistic driving data. Accident Analysis & Prevention, 2008. 40 (1): p. 8-16.
43. Layton, R. and K. Dixon, Stopping Sight Distance. Kiewit Center for Infrastructure and Transportation, Oregon Department of Transportation, 2012.
44. Ding, C. and H. Peng, Minimum redundancy feature selection from microarray gene expression data. Journal of bioinformatics and computational biology, 2005. 3 (02): p. 185-205.
45. Doerzaph, Z. R., V. Neale, and R. Kiefer. Cooperative intersection collision avoidance for violations: threat assessment algorithm development and evaluation method. in Transportation Research Board 89th Annual Meeting. 2010.
46. Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. in IJCAI. 1995.
47. James, G., et al., An introduction to statistical learning. 2013: Springer.
48. R Core Team, R: A Language and Environment for Statistical Computing. 2014, R Foundation for Statistical Computing.
49. Liaw, A. and M. Wiener, Classification and Regression by randomForest. R news, 2002. 2 (3): p. 18-22.
50. Chang, C.-C. and C.-J. Lin, LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2011. 2 (3): p. 27.