基于乘客主观感知的公交客流拥塞量化表征模型
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摘要
为了综合量化表征公交走廊客流承载设施及工具的空间拥挤性和客流运送时间效率,并科学评价客观技术指标和主观乘客感知对公交系统运行状态的敏感度,提出客流拥塞的概念,并用客流拥塞指数量化表征公交系统运行状态。通过解析客流拥塞与出行时效的关联,并分析公交出行阶段特征,基于"时效延长"思想构建客流拥塞量化表征模型,用于系统量化乘客对出行时间和空间拥挤的主观感知;选取影响出行时效的核心要素作为特征变量(车厢客流密度、站台乘客密度、区间乘车时间和站点候车时间),采用直观类比打分SP调查法获取数据用于估计模型参数;基于北京西三环公交走廊实测数据,分析全天区间客流拥塞指数时空分布特征和走廊客流拥塞指数动态演化趋势。研究结果表明:北京西三环公交走廊客流拥塞高峰阈值为0.193 9,客流拥塞状态存在明显的方向特征,下行方向客流拥塞高峰状态更显著且持续时间更长;4个特征变量灵敏度系数均值分别为0.449 2,0.165 2,1.427 1和0.408 3,即区间乘车时间为客流拥塞指数最显著的影响因子,而站台乘客密度的影响程度最小,模型识别公交客流拥塞成因的能力得以体现;该模型能够综合全面地反映公交走廊客流拥塞时空分布和动态演化趋势,并能够应用于公交运行状态改善措施研究。
To comprehensively quantify crowding and passenger transport efficiency of the equipment and facilities of a bus corridor,and scientifically evaluate the sensitivity of objective technical index and subjective passenger perception to the level of the bus running condition,the conception of passenger congestion and passenger congestion index models was proposed.From the analysis of the correlation between subjective passenger travel time and passenger congestion and the characteristics of bus travel,the passenger congestion quantification model was formulated with a time multiplier for systematically evaluating subjective perception,travel time,and crowding.A visual analogy scoring SP survey was designed for model parameter estimation data collection,incorporating four characteristic variables(passenger density in carriage,passenger density on platform,travel time,and waiting time)affecting perception time.Applying the practical data of a Beijing Xisanhuan bus corridor,the all-day temporal-spatial distribution of section passenger congestion index and the dynamic evolutionary trend of the corridor passenger congestion index were studied.According to the results,based on the peakstate threshold(the value of which is 0.193 9),the passenger congestion condition of the Xisanhuan bus corridor presents significantly directional distinction,and the peak-state is more remarkable and lasts longer in the downward direction.Furthermore,the average sensitivity coefficients of the four characteristic variables are 0.449 2,0.165 2,1.427 1,and 0.408 3,respectively,and travel time is the most significant factor of the passenger congestion index while passenger density on platform is the least,showing that the model is capable of distinguishing causes of formation.This model has the advantage of comprehensively analyzing the temporalspatial distribution and dynamic evolutionary trend of passenger congestion in a bus corridor,and may hopefully be applied to bus running condition improvement measures research.
To comprehensively quantify crowding and passenger transport efficiency of the equipment and facilities of a bus corridor,and scientifically evaluate the sensitivity of objective technical index and subjective passenger perception to the level of the bus running condition,the conception of passenger congestion and passenger congestion index models was proposed.From the analysis of the correlation between subjective passenger travel time and passenger congestion and the characteristics of bus travel,the passenger congestion quantification model was formulated with a time multiplier for systematically evaluating subjective perception,travel time,and crowding.A visual analogy scoring SP survey was designed for model parameter estimation data collection,incorporating four characteristic variables(passenger density in carriage,passenger density on platform,travel time,and waiting time)affecting perception time.Applying the practical data of a Beijing Xisanhuan bus corridor,the all-day temporal-spatial distribution of section passenger congestion index and the dynamic evolutionary trend of the corridor passenger congestion index were studied.According to the results,based on the peakstate threshold(the value of which is 0.193 9),the passenger congestion condition of the Xisanhuan bus corridor presents significantly directional distinction,and the peak-state is more remarkable and lasts longer in the downward direction.Furthermore,the average sensitivity coefficients of the four characteristic variables are 0.449 2,0.165 2,1.427 1,and 0.408 3,respectively,and travel time is the most significant factor of the passenger congestion index while passenger density on platform is the least,showing that the model is capable of distinguishing causes of formation.This model has the advantage of comprehensively analyzing the temporalspatial distribution and dynamic evolutionary trend of passenger congestion in a bus corridor,and may hopefully be applied to bus running condition improvement measures research.
引文
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[21]文婧,王星,连欣.北京市居民通勤特征研究——基于千余份问卷调查的分析[J].人文地理,2012(5):62-68.WEN Jing,WANG Xing,LIAN Xin.A Study on Commuting Characteristics in Beijing—Based on over1000Questionnaires[J].Human Geography,2012(5):62-68.
[22]贾晓朋,孟斌,张媛媛.北京市不同社区居民通勤行为分析[J].地域研究与开发,2015,34(1):55-59.JIA Xiao-peng,MENG Bin,ZHANG Yuan-yuan.Analysis of the Residents Commuting Behavior in Different Communities in Beijing City[J].Areal Research and Development,2015,34(1):55-59.
[2]DUDUTA N,SUBEDI A.Understanding Platform Overcrowding at Bus Rapid Transit Stations[C]//TRB.Proceedings of the 94th Annual Meeting of the Transportation Research Board.Washington DC:TRB,2015:118-123.
[3]EVANS G W,WENER R E.Crowding and Personal Space Invasion on the Train:Please Don’t Make Me Sit in the Middle[J].Journal of Environmental Psychology,2007,27(1):90-94.
[4]TSENG Y Y,KNOCKAERT J,VERHOEF E T.A Revealed-preference Study of Behavioral Impacts of Real-time Traffic Information[J].Transportation Research Part C,2013,30(5):196-209.
[5]HJORTH K,BORJESSON M,ENGELSON L,et al.Estimating Exponential Scheduling Preferences[J].Transportation Research Part B,2015,81:230-251.
[6]JI Y,LUO J,GEROLIMINIS N.Empirical Observations of Congestion Propagation and Dynamic Partitioning with Probe Data for Large-scale Systems[J].Transportation Research Record,2014(2422):1-11.
[7]ELHENAWY M,RAKHA H A.Congestion Prediction Using Adaptive Boosting Machine Learning Classifiers[C]//TRB.Transportation Research Board93rd Annual Meeting.Washington DC:TRB,2014:1-14.
[8]NAGORSKY B,CASELLO J,SHALABY A.Strategies to Relieve Subway Crowding:Case Study from the Toronto Context[C]//TRB.Transportation Research Board 94th Annual Meeting.Washington DC:TRB,2015:1-19.
[9]胡盼.基于人均行驶速度的路段交通拥堵指数算法[J].交通科技,2016(4):127-129.HU Pan.Traffic Congestion Index Calculation Method Based on Per Capita Travel Speed of Road Section[J].Transportation Science&Technology,2016(4):127-129.
[10]陈峻,王涛,李春燕,等.城市公交车与社会车辆混合流速度模型及交通运行状态分析[J].中国公路学报,2012,25(1):128-134.CHEN Jun,WANG Tao,LI Chun-yan,et al.Speed Models of Mixed Traffic Flow on Bus-car and Vehicle and Analysis of Traffic Running State[J].China Journal of Highway and Transport,2012,25(1):128-134.
[11]SUN D J,LIU X,NI A,et al.Traffic Congestion Evaluation Method for Urban Arterials:Case Study of Changzhou,China[J].Transportation Research Record,2014(2461):9-15.
[12]PRATO C G,RASMUSSEN T K,NIELSEN O A.Estimating Value of Congestion and Value of Reliability from the Observation of Car Drivers’Route Choice Behavior[C]//TRB.Transportation Research Board 93rd Annual Meeting.Washington DC:TRB,2014:1-16.
[13]KROES E,KOUWENHOVEN M,DEBRINCAT L,et al.The Value of Crowding on Public Transport inle-de France[C]//TRB.Transportation Research Board 93rd Annual Meeting.Washington DC:TRB,2014:1-13.
[14]LI Z,HENSHER D A,ROSE J M.Accommodating Perceptual Conditioning in the Valuation of Expected Travel Time Savings for Cars and Public Transport[J].Research in Transportation Economics,2013,39(1):270-276.
[15]牟振华,陈艳艳,陈举欣,等.基于公交IC卡数据的区域公交压力指数研究[J].交通信息与安全,2015,33(6):120-125.MOU Zhen-hua,CHEN Yan-yan,CHEN Ju-xin,et al.A Study of Zonal Transit Loading Index Based on IC Card Data[J].Journal of Transportation Information and Safety,2015,33(6):120-125.
[16]HAYWOOD L,KONING M.The Distribution of Crowding Costs in Public Transport:New Evidence from Paris[J].Transportation Research Part A,2015(77):182-201.
[17]高坤,涂辉招,李浩,等.出行者对不同交通方式行程时间可靠度和车内拥挤度的感知差异性[J].中国公路学报,2017,30(7):126-133.GAO Kun,TU Hui-zhao,LI Hao,et al.Travelers’Perception Differences in Travel Time Reliability and Crowding of Mode-choice in Multimodal Networks[J].China Journal of Highway and Transport,2017,30(7):126-133.
[18]郑远,杜豫川,孙立军.美国联邦公路局路阻函数探讨[J].交通与运输:学术版,2007(1):24-26.ZHENG Yuan,DU Yu-chuan,SUN Li-jun.Considerations on Problems in the BPR Function[J].Traffic and Transportation:Academic Edition,2007(1):24-26.
[19]维奇克.城市公共交通运营、规划与经济[M].北京:中国铁道出版社,2012.VUCHIC V R.Urban Transit:Operations,Planning,and Economics[M].Beijing:China Railway Publishing House,2012.
[20]LAM W H K,CHEUNG C Y,LAM C F.A Study of Crowding Effects at the Hong Kong Light Rail Transit Stations[J].Transportation Research Part A,1999,33(5):401-415.
[21]文婧,王星,连欣.北京市居民通勤特征研究——基于千余份问卷调查的分析[J].人文地理,2012(5):62-68.WEN Jing,WANG Xing,LIAN Xin.A Study on Commuting Characteristics in Beijing—Based on over1000Questionnaires[J].Human Geography,2012(5):62-68.
[22]贾晓朋,孟斌,张媛媛.北京市不同社区居民通勤行为分析[J].地域研究与开发,2015,34(1):55-59.JIA Xiao-peng,MENG Bin,ZHANG Yuan-yuan.Analysis of the Residents Commuting Behavior in Different Communities in Beijing City[J].Areal Research and Development,2015,34(1):55-59.