摘要
为更加准确和直观地识别快速路短交织区行驶车辆之间的交互风险,解决传统风险识别模型无法连续识别换道时车辆间交互风险的问题,本文利用无人机采集快速路短交织区内车辆轨迹数据,并利用Tracker进行读取,筛选跟驰车辆对和换道车辆对,在现有行车风险场理论的基础上,综合考虑车辆面积、车辆前进时前后方风险差异、转弯时左右方风险差异以及车辆之间横向距离,适应性改进现有行车风险场模型DRF(Driving Safety Field),利用遗传算法和波良可夫模型标定模型参数。对比改进后行车风险场模型、车头时距倒数(THWI)、碰撞时间倒数(TTCI)对车辆间跟驰交互风险与换道交互风险的识别情况,验证该模型的有效性。结果表明,本文模型较THWI和TTCI更符合驾驶员的驾驶心理,且感知风险变化先于驾驶员,对车辆换道交互风险的识别率较THWI提升52.45%,较TTCI提升83.66%,本模型在换道交互风险的识别性能上表现的更加优越。基于本文模型对短交织区区域内多车共同作用产生的风险进行可视化,可辅助交通管理部门确定需要精细化组织的关键区域,也可作为评价管理措施改善效果的可视化手段。
Traditional risk identification models cannot continuously identify the interaction risk between vehicles when changing lanes.In order to accurately and intuitively identify the interaction risk between vehicles driving in short weaving sections of expressways,this study first collects vehicle trajectory data in the short weaving area of expressways by using drones and Tracker and filters out pairs of following vehicles and pairs of lane changing vehicles.By considering the vehicle area,the risk difference between the front and rear of the vehicle when moving forward,the risk difference between the left and right when turning,and the lateral distance between vehicles,the existing driving safety field model DRF(Driving Safety Field)is adaptively improved,and the model parameters are calibrated using a genetic algorithm and the Polankov model.To verify the effectiveness of the model and calibrated parameters,the improved driving risk field model is compared with the reciprocal of headway(THWI)and the reciprocal of collision time(TTCI)in identifying the following interaction risk and lane changing interaction risk between vehicles,and verify the effectiveness of the model.The improved driving risk field model is compared with the reciprocal of headway(THWI),the reciprocal of collision time(TTCI),and the driver's recognition of the risk of following and lane changing interactions between vehicles.The results show that the proposed model consistent with the driver's driving psychology better compared to THWI and TTCI,and perceives changes in risk before the driver.The recognition rate of vehicle lane changing interaction risk is increased by 52.45%compared to THWI and 83.66%compared to TTCI.This model performs better in identifying lane changing interaction risk.Finally,based on the proposed model,the risks generated by the joint action of multiple vehicles in the short weaving area can be visualized,which can assist traffic management departments in identifying key areas that require refined organization,and can also serve as a visualization tool for evaluating the effectiveness of management measures for improvement.
作者
胡立伟
陈琛
赵雪亭
刘冰
侯智
张瑞杰
贺雨
HU Liwei;CHEN Chen;ZHAO Xueting;LIU Bing;HOU Zhi;ZHANG Ruijie;HE Yu(Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650500,China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2024年第3期221-231,共11页
Journal of Transportation Systems Engineering and Information Technology
基金
国家自然科学基金(42277476)。
关键词
交通工程
交互风险识别
行车风险场
短交织区
风险可视化
traffic engineering
interaction risk identification
driving risk field
short weaving areas
risk visualization
