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大数据驱动的地铁留乘行为分析 被引量:3

Analysis of urban subway passengers' detention behavior based on big data
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摘要 近年来地铁系统客流量急剧增加,时空上的过度拥挤造成留乘问题日益严峻。留乘严重影响了地铁运行效率,但目前缺少相关的行为学机理研究。该文使用深圳通刷卡数据,从乘客个体层面细化地铁出行过程,以乘客理性出行时间为基础识别留乘,并观测其时空分布特征。统计结果表明:深圳地铁1号线平均留乘率为12.0%,平均留乘时间为4.20 min。该文从个人、车站和线路3个层面挖掘城市地铁留乘的影响因素,提取乘客多日出行的留乘决策数据构建面板logit模型(panel logistic regression model, PLM),进一步以留乘乘客为研究对象,构建考虑个体特定效应的面板数据随机效应回归模型,探究留乘时间影响因素作用机理。结果表明:长距离出行、起始站上车和高峰时段的乘客留乘概率高,频繁出行的乘客留乘概率低;起始站上车的乘客虽有更高留乘概率,但留乘时间未显著增加,晚高峰下行方向和早高峰上行方向留乘时间分别增加0.61和0.88 min,相对运行方向留乘时间不增反降。该文挖掘了留乘行为决策机理,提高了个体留乘行为决策及留乘持续时间的预测精度,能为列车精细化调度和发布面向乘客的定制化引导策略提供依据。 [Objective]As an irreplaceable green travel mode,urban subway involves considerable daily travel.However,the passenger flow of subway systems has recently increased remarkably,resulting in overcrowding in space and time.Many trains operate at the highest frequency and maximum capacity during peak hours and still cannot meet passenger demand.Thus,detention is becoming increasingly severe,affecting the basic performance indicators of the subway systems while reducing the service quality and reliability perceived by subway passengers.However,research on the behavioral mechanism of detention is currently inadequate.Based on bus smart card data,supplemented by data mining technology,this study aims to quantify the difference between the rational travel time and the actual travel time of passengers.It reproduces the waiting process of each passenger as realistically as possible and predicts the occurrence and duration of the detention.The study provides a reference with application value for improving the future subway operation status evaluation index system and lays a research foundation for refined passenger flow distribution simulation,depending on the individual level of passengers.[Methods]The bus smart card data record the spatiotemporal information of each subway passenger's travel start and end station at a low cost.Furthermore,data mining technology is used to mine the travel information of passengers from the bus smart card data.This study uses the Shenzhen Tong data from september 2019.First,a suitable pretreatment method is adopted for the problems existing in the original data.Through an analysis of the fluctuation of each time component of the travel time,the travel time is redivided into the entry time,waiting time,in-car time,and outbound time.Second,the rational travel time value of the passenger is obtained by estimating the time of each part individually.Then the passenger's detention is identified based on the maximum rational travel time of the passenger.The influencing factors of detention are excavated from the three levels of individuals,stations,and lines.Frequent passengers are selected as research objects,and nonequilibrium panel travel data are used.Finally,a panel logistic regression model and panel data random effect regression model are established to achieve a prediction of detention and detention time.[Results]The study results,based on Shenzhen Metro Line 1,showed that the average detention rate of each station was 12.0%,the detention time was concentrated in 1.00-8.00 min,and the average detention time was 4.20 min.The travel time in the downward direction of the evening peak and the upward direction of the morning peak increased by 0.61 and 0.88 min,respectively,while the travel time in the downward direction decreased.The study also found that long-distance passengers had longer detention time than others,and they were willing to sacrifice their time in exchange for a comfortable ride.They had more choices at the starting station and were willing to wait for one or more trains to obtain seats.However,the passengers who took the subway less had cognitive bias,leading to the detention time increased.[Conclusions]Detention is a common problem during the morning and evening rush hours,which affects a passenger's travel time.This research clarifies the mechanism of detention behavior,provides a basis for generating operation management plans and issuing customized guidance strategies and is conducive to maximizing the operational efficiency of a subway transit system.
作者 王静 刘锴 王江波 宫磊 WANG Jing;LIU Kai;WANG Jiangbo;GONG Lei(School of Transportation and Logistics,Dalian University of Technology,Dalian 116024,China;College of Urban Transportation and Logistics,Shenzhen Technology University,Shenzhen 518118,China)
出处 《清华大学学报(自然科学版)》 EI CAS CSCD 北大核心 2023年第11期1781-1790,共10页 Journal of Tsinghua University(Science and Technology)
基金 国家自然科学基金资助项目(71871043) 中央高校基本科研业务费资助项目(DUT20RC(3)094) 深圳技术大学新引进高端人才财政补助科研启动经费项目(20200218) 教育部人文社会科学研究一般项目(21YJC630029) 广东省哲学社会科学规划项目(GD20CGL30)。
关键词 城市交通 留乘识别 面板logit模型 智能卡数据 urban traffic detention identification panel logistic regression model smart card data
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