|Table of Contents|

Spatial and temporal distribution characteristics of traffic accident for highway vehicle queue tail(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2014年04期
Page:
76-81,104
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Spatial and temporal distribution characteristics of traffic accident for highway vehicle queue tail
Author(s):
LI Zhi-bin1; 2;  WANG Wei1; 2;  LI Xiao-wei1; 2; 3;  WANG Hao1; 2
1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, Jiangsu, China; 2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, Jiangsu, China; 3. School of Ci
Keywords:
traffic safety;  highway;  traffic accident;  queue tail;  spatial and temporal distribution
PACS:
U491.31
DOI:
-
Abstract:
Based on the traffic flow data of recurrent congestion section on highway, the cumulative occupancy method was used to draw the fluctuating curve of traffic flow occupancy, which was used to judge the trajectory of vehicle queue tail at congestion section. The relations among occupancy, mileage position and time interval were analyzed. The inflection point of cumulative occupancy curve was determined. For the queue propagating and dissipating processes, the relations between traffic accident frequencies and temporal and sptial distances were analyzed, and the distribution features were statistically studied. Analysis result shows that when vehicle temporally and spatially approaches the queue tail, the occurrence frequency of traffic accident obviously increases, and the temporal distance and spatial distance follow the normal distribution centered on the queue tail. Normal distribution curves in different driving directions have no significant differences, but have significant differences between congestion propagation and dissipation processes. The developed joint normal distribution model of traffic accident occurring probability can be used to predict the traffic accident risks in the vicinity of queue tail, and to provide the theoretical foundation for applying dynamic traffic control for improving highway safety. 2 tabs, 12 figs, 19 refs.

References:

[1] ABDEL-ATY M, UDDIN N, PANDE A, et al. Predicting freeway crashes from loop detector data by matched case-control logistic regression[J]. Transportation Research Record, 2004(1897): 88-95.
[2] ABDEL-ATY M, UDDIN N, PANDE A. Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways[J]. Transportation Research Record, 2005(1908): 51-58.
[3] PANDE A, ABDEL-ATY M. Comprehensive analysis of the relationship between real-time traffic surveillance data and rear-end crashes on freeways[J]. Transportation Research Record, 2006(1953): 31-40.
[4] HOSSAIN M, MUROMACHI Y. Understanding crash mechanisms and selecting interventions to mitigate real-time hazards on urban expressways[J]. Transportation Research Record, 2011(2213): 53-62.
[5] XU Cheng-cheng, WANG Wei, LIU Pan. A genetic programming model for real-time crash prediction on freeways[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(2): 574-586.
[6] ZHENG Z D, AHN S, MONSERE C M. Impact of traffic oscillations on freeway crash occurrences[J]. Accident Analysis and Prevention, 2010, 42(2): 626-636.
[7] CHUNGK, JANG K, OUM S, et al. Investigation of attributes of kinematic waves preceding traffic collisions[C]∥TRB. The 90th Annual Meeting of the Transportation Research Board. Washington DC: TRB, 2011: 28-39.
[8] YEO H, JANG K, SKABARDONIS A. Impact of traffic states on freeway collision frequency[C]∥TRB. The 89th Annual Meeting of the Transportation Research Board. Washington DC: TRB, 2010: 13-27.
[9] XU Cheng-cheng, LIU Pan, WANG Wei, et al. Evaluation of the impacts of traffic states on crash risks on freeways[J]. Accident Analysis and Prevention, 2012, 47(1): 162-171.
[10] LI Zhi-bin, AHN S, CHUNG K, et al. Surrogate safety measure for evaluating rear-end collision risk related to kinematic waves near freeway recurrent bottlenecks[J]. Accident Analysis and Prevention, 2014, 64(4): 52-61.
[11] LI Zhi-bin, WANG Wei, CHEN Ruo-yun, et al. Evaluation of the impacts of speed variation on freeway traffic collisions in various traffic states[J]. Traffic Injury Prevention, 2013, 14(8): 861-866.
[12] CASSIDYM J, BERTINI R L. Some traffic features of freeway bottlenecks[J]. Transportation Research Part B: Methodological, 1999, 33(1): 25-42.
[13] CHUNG K, RUDJANAKANOKNAD J, CASSIDY M J. Relation between traffic density and capacity drop at three freeway bottlenecks[J]. Transportation Research Part B: Methodological, 2007, 41(1): 82-95.
[14] SMIRNOVN V. Tables for estimating the goodness of fit of empirical distributions[J]. The Annals of Mathematical Statistics, 1948, 19(2): 279-281.
[15] JUSTEL A, PENA D, ZAMAR R. A multivariate Kolmogorov-Smirnov test of goodness of fit[J]. Statistics and Probability Letters, 1997, 35(3): 251-259.
[16] CARLSON R C, PAPAMICHAIL I, PAPAGEORGIOU M, et al. Optimal mainstream traffic flow control of large-scale motorway networks[J]. Transportation Research Part C: Emerging Technologies, 2010, 18(2): 193-212.
[17] WANG Yi-bin, PAPAGEORGIOU M, SARROS G, et al. Feedback route guidance applied to a large-scale express ring road[J]. Transportation Research Record, 2006(1965): 79-88.
[18] LI Zhi-bin, LIU Pan, WANG Wei, et al. Development of a control strategy of variable speed limits to reduce rear-end collision risks near freeway recurrent bottlenecks[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(2): 866-877.
[19] WU Yi-hu, YU Dan, YU Wei, et al. Double-layer ramp-metering model for incident congestion on expressway[J]. Journal of Traffic and Transportation Engineering: English Edition, 2014, 1(2): 129-137.

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Last Update: 2014-08-30