A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data

Transportation Research Part C: Emerging Technologies

Volumn 54, Issue , 2015, Pages 176-186

  Sun, Jie ^a   Sun, Jian ^a  

^a Tongji University   (China)

Author keywords

Dynamic Bayesian network; Real time crash prediction; Speed conditions data; Traffic states; Urban expressway

Indexed keywords

BAYESIAN NETWORKS; FORECASTING; HIGHWAY ACCIDENTS; SMARTPHONES; SPEED;

BAYESIAN NETWORK MODELS; COMPARATIVE ANALYSIS; CRASH PREDICTION; DYNAMIC BAYESIAN NETWORKS; SPEED INFORMATION; TRAFFIC CONDITIONS; TRAFFIC STATE; URBAN EXPRESSWAY;

TRAFFIC CONGESTION;

ACCIDENT; BAYESIAN ANALYSIS; MOTORWAY; PREDICTION; TIME SERIES; TRAFFIC CONGESTION; URBAN TRANSPORT;

CHINA; SHANGHAI;

EID: 84925933796     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2015.03.006     Document Type: Article

References (39)

1. Abdel-Aty M., Pande A. Identifying crash propensity using specific traffic speed conditions. J. Safety Res. 2005, 36(1):97-108.
2. Abdel-Aty M., Uddin N., Abdalla F., Pande A., Hsia L. Predicting freeway crashes from loop detector data using matched case-control logistic regression. Transp. Res. Rec. 2004, 1897:88-95.
3. Abdel-Aty M., Uddin N., Pande A. Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways. Transp. Res. Rec. 2005, 1908:51-58.
4. Abdel-Aty M., Dilmore J., Dhindsa A. Evaluation of variable speed limits for real-time freeway safety improvement. Accid. Anal. Prev. 2006, 38(2):335-345.
5. Abdel-Aty M., Hassan H.A., Ahmed M., Al-Ghamdi A.S. Real-time prediction of visibility related crashes. Transport. Res. Part C: Emerg. Technol. 2012, 24:288-298.
6. Ahmed M., Abdel-Aty M. The viability of using automatic vehicle identification data for real-time crash prediction. IEEE Trans. Intell. Transp. Syst. 2012, 13(2):459-468.
7. Ahmed M., Abdel-Aty M., Yu R. A Bayesian updating approach for real-time safety evaluation using AVI data. Transp. Res. Rec. 2012, 2280:60-67.
8. Allaby P., Hellinga B., Bullock M. Variable speed limits: safety and operational impacts of a candidate control strategy for freeway applications. IEEE Trans. Intell. Transp. Syst. 2007, 8(4):671-680.
9. De Oña J., Mujalli R.O., Calvo F.J. Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accid. Anal. Prev. 2011, 43(1):402-411.
10. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 1977, 1-38.
11. Golob T., Recker W., Pavlis Y. Probabilistic models of freeway safety performance using traffic flow data as predictors. Saf. Sci. 2008, 46(9):1306-1333.
12. Heckerman D. A tutorial on Learning with Bayesian Networks 1998, Springer, Netherlands, pp. 301-354.
13. Hofleitner A., Herring R., Abbeel P., Bayen A. Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network. IEEE Trans. Intell. Transp. Syst. 2012, 13(4):1679-1693.
14. Hossain M., Muromachi Y. A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways. Accid. Anal. Prev. 2012, 45:373-381.
15. Hossain M., Muromachi Y. Understanding crash mechanism on urban expressways using high-resolution traffic data. Accid. Anal. Prev. 2013, 57:17-29.
16. Hossain M., Muromachi Y. A real-time crash prediction model for the ramp vicinities of urban expressways. IATSS Res. 2013, 37(1):68-79.
17. Hu, J., Sun, J., Zhao, L., 2014. Some Flow Features at Urban Expressway On-ramp Bottlenecks in Shanghai. In: Presented at the 93rd Annual Meeting of the Transportation Research Board, Washington, D.C.
18. Lee C., Saccomanno F., Hellinga B. Real-time crash prediction model for the application to crash prevention in freeway traffic. Transp. Res. Rec. 2003, 1840:67-77.
19. Lee C., Hellinga B., Saccomanno F. Assessing safety benefits of variable speed limits. Transp. Res. Rec. 2004, 1897:183-190.
20. Li Z., Wang W., Chen R., Liu P., Xu C. Evaluation of the impacts of speed variation on freeway traffic collisions in various traffic states. Traffic Injury Prevent. 2013, 14(8):861-866.
21. Liang Y., Lee J.D. A hybrid Bayesian Network approach to detect driver cognitive distraction. Transport. Res. Part C: Emerg. Technol. 2014, 38:146-155.
22. Murphy K.P. The Bayes net toolbox for matlab. Comput. Sci. Stat. 2001, 33(2):1024-1034.
23. Murphy, K.P., 2002. Dynamic Bayesian networks: representation, inference and learning (Doctoral dissertation, University of California, Berkeley).
24. Oh, C., Oh, J., Ritchie, S., Chang, M., 2001. Real-time estimation of freeway accident likelihood. In: Presented at the 80th Annual Meeting of the Transportation Research Board, Washington, D.C.
25. Pande A., Abdel-Aty M. Comprehensive analysis of the relationship between real-time traffic surveillance data and rear-end crashes on freeways. Transport. Res. Rec.: J. Transport. Res. Board 2006, 1953:31-40.
26. Pande A., Abdel-Aty M., Hsia L. Spatiotemporal variation of risk preceding crashes on freeways. Transp. Res. Rec. 2005, 1908:26-36.
27. Pande A., Das A., Abdel-Aty M., Hassan H. Real-time crash risk estimation are all freeways created equal?. Transp. Res. Rec. 2011, 2237:60-66.
28. Pande, A., Nuworsoo, C., Shew, C., 2012. Proactive Assessment of Accident Risk to Improve Safety on a System of Freeways (No. MTI Report 11-15).
29. Sun J., Sun J., Chen P. Use of support vector machine models for real-time crash risk prediction on urban expressways. Transp. Res. Rec. 2014, 2432:91-98.
30. Sun J., Zhao L., Zhang H.M. The mechanism of early-onset breakdown at Shanghai's expressway on-ramp bottlenecks. Transp. Res. Rec. 2014, 2421:64-73.
31. Tang Y., Zhang Y.Q., Chawla N.V., Krasser S. SVMs modeling for highly imbalanced classification. IEEE Trans. Syst. Man Cybern. B Cybern. 2009, 39(1):281-288.
32. Xu C., Wang W., Liu P. A genetic programming model for real-time crash prediction on freeways. IEEE Trans. Intell. Transp. Syst. 2013, 14(2):574-586.
33. Xu C., Wang W., Liu P., Guo R., Li Z. Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models. Transport. Res. Part C: Emerg. Technol. 2014, 38:167-176.
34. Xu C., Wang W., Liu P., Zhang F. Development of a real-time crash risk prediction model incorporating the various crash mechanisms across different traffic states. Traffic Injury Prevent. 2014, 16(1):28-35.
35. Yang H., Bartin B., Ozbay K. Mining the characteristics of secondary crashes on highways. J. Transport. Eng. 2014, 140(4):04013024.
36. Yeo H., Jang K., Skabardonis A., Kang S. Impact of traffic states on freeway crash involvement rates. Accid. Anal. Prev. 2013, 50:713-723.
37. Zhang K., Taylor M.A.P. Effective arterial road incident detection: a Bayesian network based algorithm. Transport. Res. Part C: Emerg. Technol. 2006, 14(6):403-417.
38. Zheng Z., Ahn S., Monsere C.M. Impact of traffic oscillations on freeway crash occurrences. Accid. Anal. Prev. 2010, 42(2):626-636.
39. Zheng, J., Sun, J., Yang, J. 2015. Relationship of lane width to capacity for urban expressways. Transp. Res. Rec., in press.