@article{0b9bdff8dcba4daebd0c7624bd09e0c2,
title = "车辆荷载特性影响下的碰撞时间分布规律",
abstract = "Time-to-collision (TTC), which is affected by vehicle load characteristics, is considered as an effective index for the risk assessment of the car-following process in collision avoidance systems. This research uses vehicle type, overweight and speeding to quantify vehicle load characteristics. Twelve types of car-following scenarios under free-flow conditions were decomposed with different load characteristics of the leading and following vehicles. The weigh-in-Motion (WIM) technique is applied to obtain traffic flow data that combines the load characteristics. The influence of vehicle load characteristics on TTC distribution in the 12 types of car-following scenarios was analyzed. The significance of TTC distribution was compared using KS test. Results show that the distribution of TTC cumulative frequency fits an exponential distribution. At the 5% level of significance, vehicle class and speeding do not show a significant effect on TTC distribution. Overweight of light vehicles has significant effect on TTC distribution. And overweight increases the potential conflict risk in the scenarios of a light vehicle following a heavy/light vehicle. In the case that the leading and following vehicles are not speeding, the potential conflict risk in the scenario of a light vehicle following a heavy vehicle is higher than the scenario of a light vehicle following a light vehicle.",
keywords = "Car-following, Load characteristics, Mathematical statistics, Surrogate measure, Time-to-collision, Traffic engineering",
author = "Ying Wang and Fang, {Zhi Chun} and Jian, {Zhu Qing} and Tu, {Hui Zhao} and Sze, {Nang Ngai}",
note = "Funding Information: (1. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China; 2. Transportation Planning and Design Institute and Limited Company of Fujian Province, Fuzhou 350000, China; 3. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hongkong 999077, China) Abstract： Time-to-collision (TTC), which is affected by vehicle load characteristics, is considered as an effective index for the risk assessment of the car-following process in collision avoidance systems. This research uses vehicle type, overweight and speeding to quantify vehicle load characteristics. Twelve types of car-following scenarios under free-flow conditions were decomposed with different load characteristics of the leading and following vehicles. The weigh-in-Motion (WIM) technique is applied to obtain traffic flow data that combines the load characteristics. The influence of vehicle load characteristics on TTC distribution in the 12 types of car-following scenarios was analyzed. The significance of TTC distribution was compared using KS test. Results show that the distribution of TTC cumulative frequency fits an exponential distribution. At the 5% level of significance, vehicle class and speeding do not show a significant effect on TTC distribution. Overweight of light vehicles has significant effect on TTC distribution. And overweight increases the potential conflict risk in the scenarios of a light vehicle following a heavy/light vehicle. In the case that the leading and following vehicles are not speeding, the potential conflict risk in the scenario of a light vehicle following a heavy vehicle is higher than the scenario of a light vehicle following a light vehicle. Keywords: traffic engineering; surrogate measure; mathematical statistics; time-to-collision; car-following; load 收稿日期：2020-04-23 修回日期：2020-07-27 录用日期：2020-08-03 基金项目：福建省交通运输科技项目/Transportation Technology Project of the Fujian Province, China(201907)；山西重点研发 计划/Major Research and Development (R&D) Project of Shanxi Province(19-JKKJ-1)；香港政府研究资助/Research Grants Council of Hong Kong(25203717). Publisher Copyright: Copyright {\textcopyright} 2020 by Science Press. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2020",
month = oct,
day = "1",
doi = "10.16097/j.cnki.1009-6744.2020.05.035",
language = "Chinese",
volume = "20",
pages = "240--246",
journal = "Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology",
issn = "1009-6744",
publisher = "Science Press",
number = "5",
}