自动预测通勤高峰期交通流量的方法  

The Automatic Prediction of Peak Commuting Traffic Flow Peak of Digital City

作　　者：许丽[1] 朱庆[1] 陈崇泰 何小波[1] 彭明军[2] 高山[2] 

机构地区：[1]西南交通大学地球科学与环境工程学院,四川成都611756 [2]武汉市国土资源和规划信息中心,湖北武汉430014

出　　处：《地理信息世界》2016年第2期16-20,共5页Geomatics World

基　　金：国家自然科学基金资助项目(41471320)资助

摘　　要：通勤高峰期的交通拥堵在中国很多城市越来越严重。通勤高峰期城市道路交通流量的准确预测是缓解交通拥堵和建设智能交通的关键基础问题之一。针对现有道路交通流量主要依靠ORIGIN-DESTINATION调查法,成本高、效率低且结果准确性有限等难题,本文提出一种低成本高效率的方法。综合利用全市域范围内的交通、社保、参保、人口等数字城市数据,依据交通工具的服务半径和出行距离,将居民的出行方式归结为步行、公汽、轨道交通和自驾4种类型,利用轨道交通优先原则和最短路径算法分析统计私家车出行数量和轨道交通人流量,并在此基础上预测通勤高峰期内的道路交通流量。以武汉市典型数据为例,证明了该方法的有效性和可靠性。Commuter peak traffic congestion becomes more and more serious in many cities. Accurately prediction of the commuter peak traffic flow can ease traffic congestion and construct intelligent transportation. Mainly relying on the existing road traffic ORIGIN-DESTINATION survey, it is high costly, low efficient and the results are limited to accuracy and other problems, so this paper propose a cost-efficient way. Utilization of transportation, social security, insurance, and population data within the scope of the digital city, and according to the vehicle＇s service radius and travel distance, a reasonable way to induct travel residents, uses the shortest path algorithm and rail transport priority algorithms to analysis, statistic the number of private cars and people in rail traffic and on these bases the road traffic in commuter peak period is forecasted. Using typical data of Wuhan City as an example the validity and reliability of the method are proved.

关 键 词：数字城市 交通流量 通勤高峰期 最短路径 轨道交通优先 

分 类 号：P208[天文地球—地图制图学与地理信息工程]