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

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.