Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2017, Vol. 51 Issue (11): 2215-2221    DOI: 10.3785/j.issn.1008-973X.2017.11.016
土木与交通工程     
高速公路强制换道持续时间半参数生存分析
吴江玲1, 张生瑞1, Amit Kumar Singh2, 秦思1, 孙振东1
1. 长安大学 公路学院, 陕西 西安 710064;
2. Atkins North America, Inc., Austin, TX, USA 78758
Semi-parametric survival analysis of mandatory lane changing duration on freeways
WU Jiang-ling1, ZHANG Sheng-rui1, SINGH Amit Kumar2, QIN Si1, SUN Zhen-dong1
1. School of Highway, Chang'an University, Xi'an 710064, China;
2. Atkins North America, Inc., Austin, TX, 78758, USA
 全文: PDF(1109 KB)   HTML
摘要：

为了分析高速公路施工作业区车辆的换道行为，运用生存分析的半参数方法，建立基于风险的车辆强制换道持续时间模型，通过无人机采集包茂高速公路陕西境内某路段施工作业区的交通数据，对道路车辆强制换道持续时间进行估计.结果表明：强制换道持续过程的生存时间大于5 s的概率约为80%，大于10 s的概率为28%，一半以上处在5~10 s之间；当距离小于400 m时，换道起点至合流点距离越近，车辆强制换道持续时间越短；当距离大于400 m时，车辆换道持续时间开始降低.车辆换道时间持续到t时刻，在至合流点距离50~100 m范围内开始换道的车辆，在下一时刻完成换道的可能性最大.
Abstract:

A hazard-based mandatory lane changing (MLC) duration model was established using the semi-parametric method of survival analysis in order to analyze vehicle's lane changing behavior characteristics on freeway work zones. Field data in work zone at Bao-mao Freeway located in Shaanxi Province were collected through an unmanned aerial vehicle. The MLC durations were estimated by analyzing the field data. Results show that the probability of MLC process survival time more than 5 s is about 80%, and the probability of survival time more than 10 s is 28%. More than half of motor vehicles' lane changing(LC) process survival time are between 5~10 s. In terms of the distance from LC initial point to the merge point, in the range of 400 m, the less it is, the shorter the MLC duration will be. As the distance increases great than 400 m, the motor vehicles' MLC duration decreases. When the MLC processes last to t, the possibility of the motor vehicles, whose LC initial points locate in 50 to 100 m away from the merging point, complete LC is the greatest in the next moment.
收稿日期: 2016-12-28 出版日期: 2017-11-13
CLC:  U491  
基金资助:

中央高校基本科研业务费资助项目（310821172202）；中国博士后科学基金资助项目（2015M582593）；云南省交通运输厅科技计划资助项目（2014（A）29）.
通讯作者: 张生瑞,男,教授.ORCID:0000-0002-9401-4686.     E-mail: zhangsr@chd.edu.cn
作者简介: 吴江玲(1987-),女,博士生,从事交通安全、智能交通等研究.ORCID:0000-0001-8736-0903.E-mail:wujiangling2006@gmail.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　

引用本文:

吴江玲, 张生瑞, Amit Kumar Singh, 秦思, 孙振东. 高速公路强制换道持续时间半参数生存分析[J]. 浙江大学学报(工学版), 2017, 51(11): 2215-2221.

WU Jiang-ling, ZHANG Sheng-rui, SINGH Amit Kumar, QIN Si, SUN Zhen-dong. Semi-parametric survival analysis of mandatory lane changing duration on freeways. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(11): 2215-2221.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2017.11.016    或    http://www.zjujournals.com/eng/CN/Y2017/V51/I11/2215

[1] RAHMAN M, CHOWDHURY M, XIE Y, et al. Review of microscopic lane-changing models and future research opportunities[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 144:1942-1956.
[2] LI X G, JIA B, JIANG R. The effect of lane-changing time on the dynamics of traffic flow[C]//International Conference on Complex Sciences. Shanghai:Springer Berlin Heidelberg, 2009:589-598.
[3] SINGH K, LI B. Discrete choice modelling for traffic densities with lane-change behavior[J]. Procedia-Social and Behavioral Sciences, 2012, 43:367-374.
[4] LI H, SHAO C, WU H, et al. A new cellular automaton model with spatiotemporal process of lane changing execution[C]//International Conference on Cellular Automata. Krakow:Springer International Publishing, 2014:605-609.
[5] 李玉洁,陈玲娟, 张光德.基于元胞自动机的施工区道路车辆换道点研究[J]. 武汉科技大学学报, 2016, 39(3):231-235. LI Yu-jie, CHEN Ling-juan, ZHANG Guang-de. Research of vehicle lane changing point on the construction area road based on cellular automata[J]. Journal of Wuhan University of Science and Technology, 2016, 39(3), 231-235.
[6] 李慧轩.基于驾驶行为动态获取的换道行为微观建模及仿真校验研究[D]. 北京交通大学, 2016. LI Hui-xuan. Research on microscopic modeling and simulation validation of lane changing behavior based on dynamic acquisition of driving behavior[D]. Beijing:Beijing Jiaotong University, doctoral dissertation, 2016.
[7] 彭非,王伟.生存分析[M].北京:中国人民大学出版社, 2004:22.
[8] JOVANIS P, CHANG H. Disaggregate model of highway accident occurrence using survival theory[J]. Accident Analysis and Prevention, 1989, (215):445-458.
[9] HOJATI A T, FERREIRA L, Washington S, et al. Hazard based models for freeway traffic incident duration[J]. Accident Analysis & Prevention, 2013, 52:171-181.
[10] LIN L, WANG Q, SADEK A W. A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations[J]. Accident Analysis & Prevention, 2016, 91:114-126.
[11] YANG X, ABDEL-ATY M, MEI H, et al. An accelerated failure time model for investigating pedestrian crossing behavior and waiting times at signalized intersections[J]. Accident Analysis & Prevention, 2015, 82:154-162.
[12] 环梅.基于生存分析的信号交叉口非机动车穿越行为研究[D]. 北京交通大学, 2014. HUAN Mei. Crossing behavior of non-motorized vehicles at urban intersections based on survival analysis method[D]. Beijing:Beijing Jiaotong University, doctoral dissertation, 2014.
[13] VLAHOGIANNI E I, KEPAPTSOGLOU K, TSETSOS V, et al. A real-time parking prediction system for smart cities[J]. Journal of Intelligent Transportation Systems, 2016, (202):192-204.
[14] YAMAMOTO T, KITAMURA R. An analysis of house hold vehicle holding durations considering intended holding durations[J]. Transportation Research Part A:Policy and Practice, 2000, (345):339-351.
[15] GARIKAPATI V, SIDHARTHAN R, PENDYALA R, et al. Characterizing household vehicle fleet composition and count by type in integrated modeling framework[J]. Transportation Research Record:Journal of the Transportation Research Board, 2014(2429):129-137.
[16] OKUSHIMA M. Multi-agent simulation for promoting clean energy vehicles from the perspective of concern for the environment and local interactions[J]. Asian Transport Studies, 2016, (41):96-113.
[17] STATHOPOULOS A, KARLAFTIS M G. Modeling duration of urban traffic congestion[J]. Journal of Transportation Engineering, 2002, (1286):587-590.
[18] 周映雪,杨小宝,环梅.基于生存分析的城市道路交通拥堵持续时间研究[J].应用数学和力学, 2013, (341). 98-106ZHOU Yin-xue, YANG Xiao-bao, HUAN Mei, et al. Survival analysis approach for estimating urban traffic congestion duration[J]. Applied Mathematics and Mechanics, 2013, (341):98-106.
[19] 杨文臣,张轮,施奕骋,等.城市快速路交通事件持续时间生存分析[J].交通运输系统工程与信息, 2014, (145):168-174. YANG Wen-chen, ZHANG Lun, SHI Yyi-cheng, et al. Survival analysis of traffic incident duration for urban expressways[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, (145), 168-174.
[20] JI Y B, JIANG R, QU M, et al. Traffic incident clearance time and arrival time prediction based on hazard models[J]. Mathematical Problems in Engineering, 2014, (20145):835-892.
[21] ZOU Y, HENRICKSON K, LORD D, et al. Application of finite mixture models for analysing freeway incident clearance time[J]. Transportmetrica A:Transport Science, 2016, (122):99-115.
[22] HOU L, LAO Y, WANG Y, et al. Time-varying effects of influential factors on incident clearance time using a non-proportional hazard-based model[J]. Transportation Research Part A:Policy and Practice, 2014, 63:12-24.
[23] YANG X, GAO Z, GUO H, et al. Survival analysis of car travel time near a bus stop in developing countries[J]. Science China Technological Sciences, 2012, (558):2355-2361.
[24] 尚山山, 钱大琳. 基于生存分析的小汽车通勤者出发时刻研究[J]. 武汉理工大学学报:交通科学与工程版, 2013, (375):1076-1079. SHANG Shan-shan, QIAN Da-lin. Car commuters departure time selection analysis based on survival analysis[J]. Journal of Wuhan University of Technology:Transportation Science & Engineering, 2013, (375), 1076-1079.
[25] DIMITRIOU L. Mechanisms of serious accidents risk mapping based on duration modeling of discretized urban continuums[C]//Hellenic Road Safety Conference. Athens:2015.
[26] HAQUE M M, WASHINGTON S. The impact of mobile phone distraction on the braking behaviour of young drivers:a hazard-based duration model[J]. Transportation Research Part C:Emerging Technologies, 2015, 50:13-27.
[27] FU C, ZHANG Y, BIE Y, et al. Comparative analysis of driver's brake perception-reaction time at signalized intersections with and without countdown timer using parametric duration models[J]. Accident Analysis & Prevention, 2015.
[28] LAWLESS J F. Statistical models and Methods for Lifetime Data[M]. New York:John Wiley & Sons, Inc., 2002:348-350.
[29] WANG L. Design, data collection, and driver behavior simulation for the Open-Mode Integrated Transportation System OMITS)[D].:Columbia University,2016.
[1] 张帅超, 朱谊, 陈喜群. 基于移动检测数据的宏观基本图特征[J]. 浙江大学学报(工学版), 2018, 52(7): 1338-1344.
[2] 李文婧, 孙锋, 李茜瑶, 马东方. 采用递归有序聚类的信号控制时段划分方法[J]. 浙江大学学报(工学版), 2018, 52(6): 1150-1156.
[3] 阮树斌, 王福建, 马东方, 金盛, 王殿海. 基于车牌识别数据的机动车出行轨迹提取算法[J]. 浙江大学学报(工学版), 2018, 52(5): 836-844.
[4] 梅振宇, 章伟. 基于复杂性测度的泊位占有率序列动力学分析[J]. 浙江大学学报(工学版), 2018, 52(4): 727-734.
[5] 龚越, 罗小芹, 王殿海, 杨少辉. 基于梯度提升回归树的城市道路行程时间预测[J]. 浙江大学学报(工学版), 2018, 52(3): 453-460.
[6] 曲昭伟, 罗瑞琪, 陈永恒, 曹宁博, 邓晓磊, 汪昆维. 信号交叉口右转机动车轨迹特性[J]. 浙江大学学报(工学版), 2018, 52(2): 341-351.
[7] 曹宁博, 陈永恒, 曲昭伟, 赵利英, 白乔文, 杨秋杰. 基于社会力模型的行人路径选择模型[J]. 浙江大学学报(工学版), 2018, 52(2): 352-357.
[8] 杨庆芳, 赵小辉, 郑黎黎, 张伟. 基于模型预测控制的环形交叉口信号配时方法[J]. 浙江大学学报(工学版), 2018, 52(1): 117-124.
[9] 杨方宜, 李铁柱. 大型综合客运枢纽送站坪交通特性及通行能力[J]. 浙江大学学报(工学版), 2017, 51(11): 2207-2214.
[10] 于德新, 田秀娟, 杨兆升, 周熙阳, 程泽阳. 改进的干线协调信号控制优化模型[J]. 浙江大学学报(工学版), 2017, 51(10): 2019-2029.
[11] 季学斌, 王慧, 宋春跃. 基于元胞自动机的施工场内交通流建模及安全分析[J]. 浙江大学学报(工学版), 2017, 51(10): 2005-2011.
[12] 李显生, 孟凡淞, 郑雪莲, 任园园, 严佳晖. 交通冲突类型对驾驶人生理特性的影响[J]. 浙江大学学报(工学版), 2017, 51(9): 1720-1726.
[13] 王薇, 程泽阳, 刘梦依, 杨兆升. 基于时空相关性的交通流故障数据修复方法[J]. 浙江大学学报(工学版), 2017, 51(9): 1727-1734.
[14] 商强, 林赐云, 杨兆升, 邴其春, 邢茹茹. 基于变量选择和核极限学习机的交通事件检测[J]. 浙江大学学报(工学版), 2017, 51(7): 1339-1346.
[15] 刘美岐, 沈莉潇, 金盛. 考虑右转信号控制的共用车道通行能力模型[J]. 浙江大学学报(工学版), 2017, 51(7): 1347-1354.