A real-time crash prediction model for the ramp vicinities of urban expressways

IATSS Research

Volumn 37, Issue 1, 2013, Pages 68-79

  Hossain, Moinul ^a   Muromachi, Yasunori ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Bayesian belief net; Ramp vicinities; Random multinomial logit; Real time crash prediction model

Indexed keywords

BAYESIAN BELIEF NETS; HAZARDOUS TRAFFIC CONDITIONS; MANEUVERING VEHICLES; MULTINOMIAL LOGIT MODEL; RAMP VICINITIES; RANDOM MULTINOMIAL LOGIT; REAL-TIME CRASH PREDICTION MODELS; VARIABLE SPEED LIMITS;

ADVANCED TRAVELER INFORMATION SYSTEMS; HIGHWAY TRAFFIC CONTROL; MANEUVERABILITY; MATHEMATICAL MODELS; TRAFFIC CONGESTION;

TRAFFIC SIGNS;

EID: 84881261981     PISSN: 03861112     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.iatssr.2013.05.001     Document Type: Article

References (32)

1. Abdel-Aty M., Pande A. Classification of real-time traffic speed patterns to predict crashes on freeways. Proceedings of the 83rd Annual Meeting of Transportation Research Board, Washington, DC 2004.
2. Abdel-Aty M., Pande A. Identifying crash propensity using specific traffic speed conditions. Journal of Safety Research 2005, 36(1):97-108.
3. Abdel-Aty M., Uddin N., Pande A. Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways. Transportation Research Record 2005, 1908:51-58.
4. Abdel-Aty M., Dilmore J., Dhindsa A. Evaluation of variable speed limits for real-time freeway safety improvement. Accident Analysis and Prevention 2006, 38(2):335-345.
5. Abdel-Aty M., Pemmanaboina R., Hsia L. Assessing crash occurrence on urban freeways by applying a system of interrelated equations. Transportation Research Record 2006, 1953:1-9.
6. Abdel-Aty M., Pande A., Das A., Knibbe W.J. Assessing safety on dutch freeways with data from infrastructural-based intelligent transportation systems. Transportation Research Record 2008, 2083:153-161.
7. Breiman L., Friedman J., Olshen R., Stone C. Classification and Regression Trees 1984, Wadsworth, Inc., Pacific Grove, CA.
8. Breiman L. Bagging predictors. Machine Learning 1996, 24(2):123-140.
9. Breiman L. Random forests. Machine Learning 2001, 45(1):5-32.
10. Dalgaard P. Introductory Statistics with R 2008, Springer, NY.
11. Dias C., Miska M., Kuwahara M., Warita H. Relationship between congestion and traffic accidents on expressways: an investigation with bayesian belief networks. Proceedings of 40th Annual Meeting of Infrastructure Planning (JSCE), Japan 2009.
12. Hossain M., Muromachi Y. Development of real-time crash prediction model by Bayesian network. Journal of Traffic Engineering, Japan Society of Traffic Engineering 2012, 42(2):39-44.
13. Hossain M., Muromachi Y. A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways. Accident Analysis and Prevention 2012, 45:373-381.
14. Hossain M., Muromachi Y. Understanding crash mechanism and selecting appropriate interventions for real-time hazard mitigation on urban expressways. Transportation Research Record 2011, 2213:53-62.
15. Hossain M., Muromachi Y. Optimum detector spacing for real-time monioring of hazardous locations on urban expressways. Journal of Infrastructure Planning Review (JSCE) 2010, 27:1045-1054.
16. Hossain M., Muromachi Y. Development of a real-time crash prediction model for urban expressway. Journal of the Eastern Asia Society for Transportation Studies 2010, 8:2092-2107.
17. Jensen F.V., Nielsen T.D. Bayesian Networks and Decision Graphs 2007, Springer, NY.
18. Jovanis P.P., Chang H.L. Modeling the relationship of accidents to miles traveled. Transportation Research Record 1986, 1068:42-51.
19. Lee C., Szccomanno F., Hellinga B. Analysis of crash precursors on instrumented freeways. Transportation Research Record 2002, 1784:1-8.
20. Lee C., Hellinga B., Saccomanno F. Real-time crash prediction model for the application to crash prevention in freeway traffic. Transportation Research Record 2003, 1840:67-77.
21. Lee C., Abdel-Aty M. Testing effects of warning messages and variable speed limits on driver behavior using driving simulator. Transportation Research Record 2008, 2069:55-64.
22. Madsen A.L., Lang M., Kjaerulff U.B., Jensen F. Hugin tool for learning Bayesian networks. Lecture Notes in Computer Science 2004, 2711:594-605.
23. Oh C., Oh J., Ritchie S., Chang M. Real-time estimation of freeway accident likelihood. Proceedings of the 80th Annual Meeting of Transportation Research Board, Washington, DC 2001.
24. Oh J., Oh C., Ritchie S., Chang M. Real time estimation of accident likelihood for safety enhancement. Journal of Transportation Engineering (ASCE) 2005, 131(5):358-363.
25. Oh C., Oh J., Ritchie S., Chang M. Real time hazardous traffic condition warning system: framework and evaluation. IEEE Transactions on Intelligent Transportation Systems 2005, 6(3):265-272.
26. Olesen K.G., Kjaerulff U., Jensen F., Jensen F.V., Falck B., Andreassen S., Andersen S.K. A MUNIN network for the median nerve - a case study on loops. Applied Artificial Intelligence 1989, 3.
27. Pande A., Abdel-Aty M. A freeway safety strategy for advanced proactive traffic management. Journal of Intelligent Transportation Systems 2005, 9(3):145-158.
28. Pande A., Abdel-Aty M. Assessment of freeway traffic parameters leading to lane-change related collisions. Accident Analysis and Prevention 2006, 38(5):936-948.
29. Prinzie A., Poel D.V. Random forests for multiclass classification: random multinomial logit. Expert Systems with Applications 2008, 34(3):1721-1732.
30. Shapire R., Freund Y., Bartlett P., Lee W. Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics 1998, 26(5):1651-1686.
31. Strobl C., Boulesteix A.L., Zeileis A., Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 2007, 8(25).
32. Zheng Z., Ahn S., Monsere C.M. Impact of traffic oscillations on freeway crash occurrences. Accident Analysis and Prevention 2010, 42(2):626-636.