混行干线车道资源—信号配时—车辆轨迹协同优化通行效率测试数据集

混行干线车道资源—信号配时—车辆轨迹协同优化通行效率测试数据由浙江大学基于SUMO仿真软件Python二次开发产生，具体针对三个连续交叉口仿真路网，通过仿真得到路网平均旅行时间、平均延误时间、平均速度、平均排队时间、平均等待时间和车辆等待总次数等数据。共得到2套控制方案下，3种不同交通需求水平下的6组测试结果数据。

The mixed-flow arterial lane resource-signal timing-vehicle trajectory collaborative optimization traffic efficiency test dataset was generated by Zhejiang University via secondary development of the SUMO simulation software with Python. It is targeted at a simulated road network with three consecutive intersections, and data including the average travel time, average delay time, average speed, average queuing time, average waiting time and total number of vehicle waiting times of the road network were obtained through simulation. A total of 6 sets of test result data were acquired under 2 control schemes and 3 different traffic demand levels.

搜集汇总

背景与挑战

背景概述

该数据集由浙江大学基于SUMO仿真软件Python二次开发产生，针对三个连续交叉口仿真路网，通过仿真获取路网平均旅行时间、平均延误时间和平均速度等通行效率指标。数据集包含2套控制方案和3种交通需求水平下的6组测试结果，旨在测试混行干线车道资源、信号配时和车辆轨迹的协同优化方法，属于国家重点研发计划项目的一部分。