Real-time traffic safety evaluation method for freeway in fog

doi:10.16265/j.cnki.issn1003-3033.2017.04.020

Abstract

Abstract: To evaluate the real-time safety status of a freeway in fog, the VISSIM software was used to simulate freeway traffic flows in various foggy environments, based on the actual traffic data, and the relationships between visibility, traffic flow parameters and real-time traffic safety were analyzed. The number of traffic conflicts was selected as indicator of freeway real-time safety status, and the main factors influencing real-time traffic safety status were identified using the random forest method, then a calculation model was built for real-time traffic conflicts by regression analysis. The model was tested and verified by using the traffic and meteorology data for I-43 freeway in USA. The results show that the traffic conflicts calculated by the model are consistent with the occurrence of traffic accidents, which can accurately reflect traffic safety status before and after the accidents under a foggy weather condition.

Key words: freeway, real-time traffic safety, random forest, foggy environment, traffic conflict

CLC Number:

X951

ZHANG Cunbao, PENG Hanhui, ZHANG Shan, LYU Changping. Real-time traffic safety evaluation method for freeway in fog[J]. China Safety Science Journal, 2017, 27(4): 110-115.

References

[1] 公安部交通管理局.2014中华人民共和国道路交通事故统计年报[M].北京:公安部交通管理局,2015:70.
Traffic Management Bureau of the Public Security Ministry. 2014 annual report of the People's Republic of China on road traffic accident statistics[M]. Beijing: Traffic Management Bureau of the Public Security Ministry, 2015: 70.
[2] SNOWDEN R J, STIMPSON N, RUDDLE R A. Speed perception fogs up as visibility drops[J]. Nature, 1998, 392(6675): 450.
[3] ABDELATY M, EKRAM A, HUANG H, et al. A study on crashes related to visibility obstruction due to fog andsmoke[J]. Accident Analysis and Prevention, 2011, 43(5): 1 730-1 737.
[4] 胡思涛,王雪梅,朱艳茹.团雾天气下高速公路交通事故特征分析[J].交通信息与安全,2014,32(4):69-73.
HU Sitao, WANG Xuemei, ZHU Yanru. Characteristics analysis of freeway traffic crashes under dumpling fog[J]. Journal of Transportation Information and Safety, 2014, 32(4): 69-73.
[5] 杨鑫宇.雾对高速公路行车的影响及对策研究[D].西安:长安大学,2014.
YANG Xinyu. Study on the influence of fog on expressway and countermeasures[D]. Xi'an: Chang'an University, 2014.
[6] OH J S, OH C, RITCHIE S G, et al. Real-time estimation of accident likelihood for safety enhancement[J]. Journal of Transportation Engineering, 2005, 131(5): 358-363.
[7] LEE C, HELLINGA B, SACCOMANNO F. Real-time crash prediction model for application to crash prevention in freeway traffic[J]. Transportation Research Record Journal of the Transportation Research Board, 2003, 1840: 66-77.
[8] AHMED M, ABDEL-ATY M. The viability of using automatic vehicle identification data for real-time crash prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 459-468.
[9] 江周,张存保,夏银霞.基于交通冲突的高速公路实时安全状态评价研究[J].中国安全科学学报,2014,24(9):95-101.
JIANG Zhou, ZHANG Cunbao, XIA Yinxia. Research on real-time traffic safety state evaluation of freeway based on number of traffic conflicts[J]. China Safety Science Journal, 2014, 24(9): 95-101.
[10] ABDEL-ATY M, HASSAN H M, AHMED M, et al. Real-time prediction of visibility related crashes[J]. Transportation Research Part C: Emerging Technologies, 2012, 24(9): 288-298.
[11] YU Rongjie, ABDEL-ATY M, AHMED M. Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors[J]. Accident Analysis and Prevention, 2013, 50(2): 371-376.
[12] HASSAN H M, ABDEL-ATY M. Predicting reduced visibility related crashes on freeways using real-time traffic flow data[J]. Journal of Safety Research, 2013, 45(6): 29-36.
[13] XU Chengcheng, WANG Wei, LIU Pan. Identifying crash-prone traffic conditions under different weather on freeways[J]. Journal of Safety Research, 2013, 46: 135-144.
[14] 项乔君,陆键,卢川,等.道路交通冲突分析技术及应用[M].北京:科学出版社,2008:1-7.
XIANG Qiaojun, LU Jian, LU Chuan, et al. Road traffic conflict analysis technology and application[M]. Beijing: Science Press, 2008: 1-7.
[15] MOHAMMED S O, MALGORZATA J R, ANDREW R. Evaluation of overhead guide sign sheeting materials to increase visibility and safety for drivers[J]. Applied Ergonomics, 2016, 56: 136-143.
[16] 冯民学,顾松山,卞光辉.高速公路浓雾监测预警系统[J].中国公路学报,2004,17(3):92-97.
FENG Minxue, GU Songshan, BIAN Guanghui. Study of heavy fog monitoring and warning system for freeway[J]. China Journal of Highway and Transport, 2004, 17(3): 92-97.
[17] XU Chengcheng, ANDREW P T, WANG Wei. Predicting crash likelihood and severity on freeways with real-time loop detector data[J]. Accident Analysis and Prevention, 2013, 57(1): 30-39.
[18] LIN Lei, WANG Qian, SADEK A W. A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction[J]. Transportation Research Part C, 2015, 55: 444-459.
[19] BREIMAN L. Random forests[J]. Machines Learning, 2001, 45(1): 5-32.