姚文彬,戎栋磊,胡佑薇,苏弘扬,陈诺,金盛

• 1:浙江大学
• 2:建筑工程学院
• 3:浙江大学
• 4:平衡建筑研究中心

摘要(Abstract)：

基于交通时空大数据的微观出行行为分析可以为精细化、个性化的交通管控措施制定提供支持，车牌识别数据作为一种精度高、准确性高、采样率全的时空大数据，近年来受到广泛关注。但是现有基于车牌识别数据的出行行为分析文献在进行行为分析的过程中较少考虑路网特征，即没有将出行者的行为与路网特性结合起来分析，这导致挖掘得到的出行模式与路网本身的关联不高。本文首先对车牌识别数据和路网拓扑数据进行数据融合，基于此融合数据，根据机动化出行者的出行行为特性使用聚类算法进行车辆画像，将路网上的车辆划分为临时办事车辆、频繁过境车辆、家庭不常用车辆、通勤车辆、网约出租车辆五类车辆。同时，结合复杂网络方法和聚类算法对交叉口进行画像分析，挖掘出交通管理者需要重点关注的交叉口。在此基础上，结合车辆出行行为和路网拓扑信息深入挖掘出行车辆的出行模式，构建车辆画像-交叉口画像-过车频次矩阵、车辆画像-交叉口画像-过车占比矩阵，进而对车辆出行时空特性进行深入挖掘，为交通管控措施的制定提供支持。

关键词(KeyWords)： 智能交通;出行行为;画像分析;车牌识别数据;交通管控

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目（92046011）;; 浙江省“尖兵“”领雁”研发攻关计划项目（2022C01050）

作者(Author): 姚文彬,戎栋磊,胡佑薇,苏弘扬,陈诺,金盛

DOI: 10.19961/j.cnki.1672-4747.2022.09.011

参考文献(References)：

• [1]欧梦宁,张利国.基于智能手机应用的城市交通信息检测技术研究进展[J].交通运输工程与信息学报, 2016,14(2):128-136.OU Meng-ning, ZHANG Li-guo. Research advances in urban traffic information detection technologies based on smartphone applications[J]. Journal of Transportation Engineering and Information, 2016, 14(2):128-136.
• [2]孟永平.基于多源数据融合的厦门市现状交通模型构建及应用[J].交通运输工程与信息学报, 2020, 18(4):138-144.MENG Yong-ping. Current traffic model in Xiamen based on multi-source data fusion, construction, and application[J]. Journal of Transportation Engineering and Information, 2020, 18(4):138-144.
• [3]刘澜,卢维科,尹俊淞.城市交通拥挤对策新解[J].交通运输工程与信息学报, 2014, 12(4):1-7.LIU Lan, LU Wei-ke, YIN Jun-song. New interpretation on urban traffic congestion countermeasures[J]. Journal of Transportation Engineering and Information, 2014, 12(4):1-7.
• [4]向师仲,李建海,李敏,等.云计算在智能交通中的应用[J].交通运输工程与信息学报, 2015, 13(2):45-49.XIANG Shi-zhong, LI Jian-hai, LI Min, et al. Cloud computing applications in intelligent transportation[J]. Journal of Transportation Engineering and Information, 2015, 13(2):45-49.
• [5] SUN S, YANG D. Identifying public transit commuters based on both the smartcard data and survey data:a case study in Xiamen, China[J]. Journal of Advanced Transportation, 2018, 2018(2063):1-10.
• [6] MA X, LIU C, WEN H, et al. Understanding commuting patterns using transit smart card data[J]. Journal of Transport Geography, 2017, 58:135-145.
• [7] LUO X, WANG D, MA D, et al. Grouped travel time estimation in signalized arterials using point-to-point detectors[J]. Transportation Research Part B:Methodological,2019, 130:130-151.
• [8] MA X, WU Y J, WANG Y, et al. Mining smart card data for transit riders’travel patterns[J]. Transportation Research Part C:Emerging Technologies, 2013, 36:1-12.
• [9] YAO W, CHEN C, SU H, et al. Analysis of key commuting routes based on spatiotemporal trip chain[J]. Journal of Advanced Transportation, 2022, 2022:1-15.
• [10] GAO J, SUN L, CAI M. Quantifying privacy vulnerability of individual mobility traces:a case study of license plate recognition data[J]. Transportation Research Part C:Emerging Technologies, 2019, 104:78-94.
• [11] YAO W, YU J, YANG Y, et al. Understanding travel behavior adjustment under COVID-19[J]. Communications in Transportation Research, 2022, 2:100068.
• [12] YAO W, ZHANG M, JIN S, et al. Understanding vehicles commuting pattern based on license plate recognition data[J]. Transportation Research Part C:Emerging Technologies, 2021, 128(2):103142.
• [13] CHEN H, YANG C, XU X. Clustering vehicle temporal and spatial travel behavior using license plate recognition data[J]. Journal of Advanced Transportation, 2017,2017:1-14.
• [14] CHANG Y, DUAN Z, YANG D. Using ALPR data to understand the vehicle use behaviour under TDM measures[J]. IET Intelligent Transport Systems, 2018, 12(10):1264-1270.
• [15] SHEN X, ZHOU Y, JIN S, et al. Spatiotemporal influence of land use and household properties on automobile travel demand[J]. Transportation Research Part D:Transport and Environment, 2020, 84:102359.
• [16] LIU Z, LI R, WANG X, et al. Effects of vehicle restriction policies:analysis using license plate recognition data in Langfang, China[J]. Transportation Research Part A:Policy and Practice, 2018, 118:89-103.
• [17] YAO W, DING Y, XU F, et al. Analysis of cars’commuting behavior under license plate restriction policy:a case study in Hangzhou, China[C]//International Conference on Intelligent Transportation Systems(ITSC). Maui:IEEE, 2018:236-241.
• [18] SUN L J, CHEN X Y, HE Z C, et al. Routine pattern discovery and anomaly detection in individual travel behavior[J]. Networks and Spatial Economics, 2021:1-22.
• [19] ZHAO Y, ZHU X, GUO W, et al. Exploring the weekly travel patterns of private vehicles using automatic vehicle identification data:a case study of Wuhan, China[J].Sustainability, 2019, 11(21):6152.
• [20]畅玉皎,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, 112.CHANG Yu-jiao, YANG Dong-yuan. Recognition of vehicles with commuting property using license plate data[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(2):77-82, 112.
• [21]王宇.基于卡口车牌识别数据的违法出租车时空特征分析与区域分布预测研究[D].广州:广东工业大学,2022.WANG Yu. Spatial and temporal characteristics analysis and regional distribution prediction of illegal taxis using license plate recognition data[D]. Guangzhou:Guangdong University of Technology, 2022.
• [22]赵雨慧.基于卡口车牌识别数据的出行模式挖掘与驾驶员画像研究[D].武汉:武汉大学, 2019.ZHAO Yu-hui. Travel pattern and private drivers’profile based on license plate recognition data[D]. Wuhan:Wuhan University, 2019.
• [23]蔡正义.基于大数据的城市居民出行分析建模[D].杭州：浙江大学,2018.CAI Zheng-yi. Analysis and modeling of urban mobility based on big data[D]. Hangzhou:Zhejiang University,2018.
• [24] BOEING G. OSMnx:new methods for acquiring, constructing, analyzing, and visualizing complex street networks[J]. Computers, Environment and Urban Systems,2017, 65:126-139.
• [25] ROUSSEEUW P J. Silhouettes:a graphical aid to the interpretation and validation of cluster analysis[J]. Journal of Computational and Applied Mathematics, 1987, 20:53-65.
• [26]王玺翔,崔欣,刘鹏.交通网络脆弱性研究方法对比与分析[C]//2022世界交通运输大会(WTC2022)论文集（运输规划与交叉学科篇）.武汉:WTC, 2022:356-362.
• [27]刘箫.基于复杂网络理论的城市轨道交通网络脆弱性研究[D].西安：长安大学, 2020.LIU Xiao. Research on urban rail transit network vulnerability based on complex network theory[D]. Xi’an:Chang’an University, 2020.