数据驱动的收费站运行参数预测及车道配置  

Operation parameter prediction and lane allocation optimization of toll station driven by data

作　　者：李君羡[1] 周一晨 沈宙彪 张珏蓉 吴志周[1] LI Jun-xian;ZHOU Yi-chen;SHEN Zhou-biao;ZHANG Jue-rong;WU Zhi-zhou(Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University,Shanghai 201804,China;Shanghai Naite Expressway Toll Settlement Co.,Ltd.,Shanghai 200063,China;Shanghai Urban Construction Design and Research Institute(Group)Co.,Ltd.,Shanghai 200125,China)

机构地区：[1]同济大学道路与交通工程教育部重点实验室,上海201804 [2]上海耐特高速公路收费结算有限公司,上海200063 [3]上海市城市建设设计研究总院(集团)有限公司,上海200125

出　　处：《交通科学与工程》2021年第1期95-103,共9页Journal of Transport Science and Engineering

基　　金：国家自然科学基金项目资助(61773288)。

摘　　要：为优化电子不停车收费系统(ETC)推广期间收费站车道配置。依据数据驱动,提出了预测指定日期ETC和人工半自动收费车道(MTC)小时最高流量的方法。结合排队论和驾驶员在收费站选择车道的行为,提出了收费站车道资源配置的优化,并以上海市112个收费站实际数据为例进行验证。研究结果表明:所提出的预测收费站日流量、小时最高流量及ETC流量占比趋势等方法可行,可为收费站车道资源配置提供借鉴。To study lane allocation optimization during the ETC promotion period,a data-driven method was presented to predict ETC and MTC volumes in the peak hour of a coming day.Combined with queue theory and drivers’behavior of choosing toll lanes,a method was proposed to optimize the lane allocation scheme.Actual operation data of 112 toll stations in Shanghai were collected to illustrate the manipulation.The outcome suggests that methods proposed are applicable to predict the daily flow of toll stations,the highest-volume hour of toll station,and the growing trend of ETC traffic,which could contribute to decide the lane allocation scheme.

关 键 词：数据驱动 收费站 车道配置 排队论 电子不停车收费系统 

分 类 号：U491.4[交通运输工程—交通运输规划与管理]