基于时间特征行为动力学的通勤模式分析
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摘要
公共交通是解决城市交通拥堵问题的有效措施.通勤群体作为城市公共交通的主要参与者,其行为模式一直以来是学界的研究重点与难点.本研究以全样本出行数据为基础,采用人类行为动力学方法,通过分析通勤乘客的出行时间间隔分布特征,从群体层面实证了人类行为"幂律呈现"的普适性,但同时也在部分个体样本上观察到了"幂律消散"现象.深入研究发现,这些偏离幂律拟合特征的个体,一般具有更为规则的出行规律.进一步提出了基于曲线拟合参数及出行间隔谱函数的通勤乘客聚类方法,直观、有效地解决了通勤乘客的细分问题.珠海市的案例显示,对于深入研究城市公共交通出行规律具有很强的实用价值.
Public transportation is an effective measure to solve urban traffic congestion problems.As a major participant in urban public transportation,commuting groups and their behavioral patterns have long been the focus of academic research.The study constructed the passenger bus travel time interval distribution with behavior dynamics method based on the whole sample travel data.It demonstrates the universality of "power law representation" of human behavior from the group level.But at the same time,the phenomenon of "power law dissipation" has been observed in some individual samples.In-depth study finds that the individuals deviating from power law fitting characteristics have more regular travel rules generally.Further,the clustering methods of commuter passengers was proposed based on curve fitting parameters and travel interval spectral function.The methods can solve the subdivision problem of commuter passengers intuitively and effectively.The case of Zhuhai shows that the research has a strong practical value for in-depth study of urban public transport travel rules.
Public transportation is an effective measure to solve urban traffic congestion problems.As a major participant in urban public transportation,commuting groups and their behavioral patterns have long been the focus of academic research.The study constructed the passenger bus travel time interval distribution with behavior dynamics method based on the whole sample travel data.It demonstrates the universality of "power law representation" of human behavior from the group level.But at the same time,the phenomenon of "power law dissipation" has been observed in some individual samples.In-depth study finds that the individuals deviating from power law fitting characteristics have more regular travel rules generally.Further,the clustering methods of commuter passengers was proposed based on curve fitting parameters and travel interval spectral function.The methods can solve the subdivision problem of commuter passengers intuitively and effectively.The case of Zhuhai shows that the research has a strong practical value for in-depth study of urban public transport travel rules.
引文
[1] 邵春福.交通规划原理 [M].2版.北京:中国铁道出版社,2014.
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[11] 呙娟.基于公交数据的乘客出行特征分析 [D].广州:华南理工大学,2015.
[12] 刘永鑫.基于多源数据融合的城市公交系统乘客出行模式挖掘及其应用研究 [D].广州:华南理工大学,2018.
[13] WENG Xiaoxiong,LIU Yongxin,SONG Houbing,et al.Mining urban passengers' travel patterns from incomplete data with use cases [J].Computer Networks,2018,134:116- 126.
[2] SUN Lijun,LEE Der-Horng,ERATH Alex,et al.Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system [C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Beijing:KDD,2012.
[3] 陈绍辉,陈艳艳,赖见辉.基于GPS与IC卡数据的公交站点匹配方法 [J].公路交通科技,2012,29(5):102- 108.CHEN Shaohui,CHEN Yanyan,LAI Jianhui.An approach on station ID and trade record match based on GPS and IC card data [J].Journal of Highway and Transportation Research and Development,2012,29(5):102- 108.
[4] 王超.基于IC卡信息的公交客流OD推算方法研究 [D].北京:北京交通大学,2012.
[5] ALEXEI Vazquez.Exact results for the Barabasi model of human dynamics [J].Physical Review Letters,2005,95(24):248701.
[6] ALBERT-LASZLO Barabasi.The origin of bursts and heavy tails in human dynamics [J].Nature,2005,435:207- 211.
[7] ROBERT Schlich,KAY W Axhausen.Habitual travel behavior:evidence from a six-week travel diary [J].Transportation,2003,30:13- 36.
[8] 周涛,韩筱璞,闫小勇,等.人类行为时空特性的统计力学 [J].电子科技大学学报,2013,42(4):481- 540.ZHOU Tao,HAN Xiaopu,YAN Xiaoyong,et al.Statistical mechanics on temporal and spatial activities of human [J].Journal of University of Electronic Science and Technology of China,2013,42(4):481- 540.
[9] 李楠楠,周涛,张宁.人类动力学基本概念与实证分析 [J].复杂系统与复杂性科学,2008,5(2):15- 24.LI Nannan,ZHOU Tao,ZHANG Ning.Concept and empiri-cal results of human dynamics [J].Complex Systems and Complexity Science,2008,5(2):15- 24.
[10] 韩筱璞,汪秉宏,周涛.人类行为动力学研究 [J].复杂系统与复杂性科学,2010,7(2/3):132- 144.HAN Xiaopu,WANG Binghong,ZHOU Tao.Researches of human dynamics [J].Complex Systems and Complexity Science,2010,7(2/3):132- 144
[11] 呙娟.基于公交数据的乘客出行特征分析 [D].广州:华南理工大学,2015.
[12] 刘永鑫.基于多源数据融合的城市公交系统乘客出行模式挖掘及其应用研究 [D].广州:华南理工大学,2018.
[13] WENG Xiaoxiong,LIU Yongxin,SONG Houbing,et al.Mining urban passengers' travel patterns from incomplete data with use cases [J].Computer Networks,2018,134:116- 126.