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基于高速公路流水数据的通勤车辆特征研究
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  • 英文篇名:Trip Characteristics of Vehicle with Commuting Property Based on Highway Ticket Data
  • 作者:魏广奇 ; 苏跃江 ; 吴德馨 ; 袁敏贤
  • 英文作者:WEI Guang-qi;SU Yue-jiang;WU De-xin;YUAN Min-xian;Guangzhou Transport Research Institute;Guangzhou Public Transport Research Center;
  • 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
  • 英文关键词:urban traffic;;highway toll ticket data;;commuting identification;;feature clustering
  • 中文刊名:交通运输系统工程与信息
  • 英文刊名:Journal of Transportation Systems Engineering and Information Technology
  • 机构:广州市交通运输研究所;广州市公共交通研究中心;
  • 出版日期:2019-06-15
  • 出版单位:交通运输系统工程与信息
  • 年:2019
  • 期:03
  • 语种:中文;
  • 页:241-248
  • 页数:8
  • CN:11-4520/U
  • ISSN:1009-6744
  • 分类号:U491
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 spacetime 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.
引文
[1]李树彬,党文修,傅白白.基于收费数据的高速公路实时网络状态估计研究[J].交通运输系统工程与信息,2015, 15(4):63-69.[LI S B, DANG W X, FU B B.Traffic real-time network states estimation of expressway based on toll data[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(4):63-69.]
    [2]沈强.基于高速公路收费数据的路网运行状态评价[J].公路交通科技, 2012, 29(8):118-126.[SHEN Q.Road network mobility performance evaluation based on freeway toll data[J]. Journal of Highway and Transportation Research and Development, 2012, 29(8):118-126.]
    [3]郭瑞军,于景,孙晓亮.基于电子收费数据的高速公路交通流特性分析[J].大连交通大学学报, 2018, 1(1):17-22.[GUO R J, YU J, SUN X L. Analysis on traffic flow character of expressway based on electric charge data[J]. Journal of Dalian Jiaotong University, 2018, 1(1):17-22.]
    [4]杨庆芳,马明辉,梁士栋,等.基于收费数据的高速公路交通状态判别方法[J].华南理工大学学报(自然科学版), 2014, 42(12):51-57.[YANG Q F, MA M H,LIANG S D, et al. Highway traffic status discrimination method based on toll data[J]. Journal of South China University of Technology, 2014, 42(12):51-57.]
    [5]刘伟铭,李松松.大数据中高速公路旅行时间预测仿真研究[J].计算机仿真, 2017, 34(3):395-399.[LIU W M, LI S S. Freeway travel time prediction simulation research based on big data[J]. Computer Simulation,2017, 34(3):395-399.]
    [6]畅玉姣,杨东援.基于车牌照数据的通勤特征车辆识别研究[J].交通运输系统工程与信息, 2016, 16(2):77-82.[CHANG Y J, YANG D Y. Recognition of vehicles with commuting property using license plate data[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(2):77-82.]
    [7] ARTHUR D, VASSILVITSKII S. K-means++:The advantages of careful seeding[C]//Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2007:1027-1035.

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