摘要
随着居民利用高速公路进行通勤出行车辆的增加,高速公路缓行和交通拥堵等问题时有发生,特别是在重大节假日期间.目前,解决上述交通问题的主要方法是交通需求管理措施,而实现有针对性的交通需求管理需要对高速公路收费流水数据进行精确的挖掘分析,掌握车辆在高速公路上的运行状态与时空分布特征.本文基于高速公路收费流水数据,借助K-means++聚类方法识别使用高速公路日常通勤的车辆,进一步分析通勤车辆的出行时空分布特征.从通勤出行的角度,挖掘城市通勤快速出行廊道分布,研究高速公路网与城市道路网络的关系,对提高交通系统效率和缓解交通问题具有重要的意义.
The use of high-speed commuter vehicles has also increased especially during major holidays, and traffic problems such as high-speed slow-moving and congestion have occurred. At present, the main method to solve the above traffic problems is traffic demand management, and the realization of targeted traffic demand management requires mining and analysis of highway toll ticket data, and grasping the running state and space- time distribution characteristics of vehicles on the expressway. Based on the highway toll ticket data, this paper uses the K-means++ clustering method to identify the commuter vehicles which is using the highway, and further analyzes the time and space distribution characteristics of the commuter vehicles. From the perspective of commuting, it is of great significance to explore the distribution of urban commuter vehicles’rapid travel corridors and study the relationship between highway network and urban road network, which is to improve the efficiency of urban transportation system and alleviate traffic problems.
作者
魏广奇
苏跃江
吴德馨
袁敏贤
WEI Guang-qi;SU Yue-jiang;WU De-xin;YUAN Min-xian(Guangzhou Transport Research Institute, Guangzhou 510000, China;Guangzhou Public Transport Research Center, Guangzhou 510000, China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2019年第3期237-244,共8页
Journal of Transportation Systems Engineering and Information Technology
关键词
城市交通
高速公路收费流水数据
通勤识别
特征聚类
urban traffic
highway toll ticket data
commuting identification
feature clustering