Rear-end crash potential estimation in the work zone merging areas

Journal of Advanced Transportation

Volumn 48, Issue 3, 2014, Pages 238-249

  Weng, Jinxian ^a   Meng, Qiang ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

logit model; merging; rear end crash; time to collision; work zone merging area

Indexed keywords

MATHEMATICAL MODELS; MERGING; SPEED CONTROL; VEHICLES;

BINARY LOGIT MODEL; CRASH POTENTIAL; LOGIT MODELING; MERGING BEHAVIOR; REAR-END CRASHES; TIME-TO-COLLISION; TRAFFIC SAFETY; WORK ZONES;

ACCIDENTS;

EID: 84898543729     PISSN: 01976729     EISSN: 20423195     Source Type: Journal    
DOI: 10.1002/atr.211     Document Type: Article

References (19)

1. Wang J, Hughes WE, Council FM, Paniati JF,. Investigation of highway work zone crashes: what we know and what we don't know. Transportation Research Record 1996; 1529: 54-62. (Pubitemid 126527566)
2. Rouphail NM, Yang ZS, Fazio J,. Comparative study of short- and long-term urban freeway work zones. Transportation Research Record 1988; 1163: 4-14.
3. Khattak AJ, Khattak AJ, Council FM,. Effects of work zone presence on injury and non-injury crashes. Accident Analysis and Prevention 2002; 34-1: 19-29. (Pubitemid 32951182)
4. Garber NJ, Zhao M,. Distribution and characteristics of crashes at different work zone locations in Virginia. Transportation Research Record 2002; 1794: 19-25.
5. Meng Q, Weng J,. A genetic algorithm approach to assessing work zone casualty risk. Safety Science 2011; 49: 1283-1288.
6. Srinivasan S, Carrick G, Heaslip K, Washburn S,. Analysis of crashes in freeway work-zone queues: a case study. Research Project #: 07-UF-R-S3, 2007.
7. Oh C, Park S, Ritchie SG,. A method for identifying rear-end collision risks using inductive loop detectors. Accident Analysis and Prevention 2006; 38 (2): 295-301. (Pubitemid 43121253)
8. Oh C, Kim T,. Estimation of rear-end crash potential using vehicle trajectory data. Accident Analysis and Prevention 2010; 42 (6): 1888-1893.
9. Tay R, Rifaat SM,. Factors contributing to the severity of intersection crashes. Journal of Advanced Transportation 2007; 41: 245-265.
10. Barua, U, Tay, R,. Severity of urban transit bus crashes in Bangladesh. Journal of Advanced Transportation 2010; 44: 34-41.
11. Rifaat SM, Tay R, de Barros AG,. Logistic model of injury risks in single vehicle crashes in urban neighborhoods. Journal of Advanced Transportation 2011; doi: 10.1002/atr.164
12. Harb R, Radwan E, Yan X, Pande A, Abdel-Aty M,. Freeway work-zone crash analysis and risk identification using multiple and conditional logistic regression. Journal of Transportation Engineering 2008; 134 (5): 203-214. (Pubitemid 351566769)
13. Hu W, Xiao X, Xie D,. Traffic accident prediction using 3-D model based vehicle tracking. IEEE Transactions on Vehicular Technology 2004; 53 (3): 677-694.
14. Hourdos J, Garg V, Michalopoulos PG, Davis GA,. Real-time detection of crash-prone conditions at freeway high-crash locations. Transportation Research Record 2006; 1968: 83-89.
15. Oh C, Oh J, Min J,. Real-time detection of hazardous traffic events on freeways. Transportation Research Record 2009; 2129: 35-44.
16. Kita H,. Effects of merging lane length on the merging behavior at expressway on-ramps. Proceedings of the 12th International Symposium on the Theory of Traffic Flow and Transportation 1993; 37-51.
17. Weng J, Meng Q,. Modeling speed-flow relationship and merging behavior at work zone merging areas. Transportation Research Part C: Emerging Technologies 2011; 19 (6): 985-996.
18. Hyden C,. Traffic conflicts technique: state of the art. In:, Topp HH,. (Ed.) Traffic safety work with video processing. University Kaiserslautern, Transportation Department, Green Series 1996; 37: 3-14.
19. SAS. SAS/STAT 9.2 User's Guide. Cary, NC: SAS Publishing, 2008.