基于刷卡数据的公交-地铁换乘模式研究

Bus-Subway Interchange Mode Research with IC Card Data

随着城市规模的不断扩大,城市居民通勤中混合交通模式普遍出现,即需要借助不同交通工具之间的换乘完成行程。精确提取和分析城市居民换乘行为,对城市交通模式及设施便捷性等研究具有重要意义。目前,换乘行为的提取多采用GPS(Global Positioning System)、GTFS(General Transit Feed Specification)等数据,基于步行速度或经验选取距离阈值或时间阈值,进而实现换乘行为的识别。但这种方式忽略了城市空间内公交或地铁站点密度的差异性特征,识别精度可能受到较大影响。因此,本研究基于公交地铁IC卡数据,提出了一种时间-距离阈值双约束的换乘行为识别算法,即根据公共交通刷卡数据的统计特征,实现时间和距离阈值的自动选择,进而精准提取换乘行为。在此基础上,本文根据前后半程的旅行时间/距离长短将换乘行为分为九类换乘模式:长-长换乘、长-中换乘、长-短换乘、中-长换乘、中-中换乘、中-短换乘、短-长换乘、短中换乘、短-短换乘,并分别对其出行特征进行分析。结果表明,所有类型的换乘行为的早高峰均早于公交和地铁的出行早高峰,短-长换乘的早高峰时间甚至比一般出行的早高峰时间提前了30 min,充分说明了以换乘模式通勤的乘客需要付出更大的努力。相比之下,晚高峰出行时间则各有早晚,如长-长、长-短换乘模式晚高峰明显滞后于一般出行的晚高峰时间,更凸显了换乘群体的通勤成本负担之重。从出行距离上来说,九种换乘行为的通勤距离峰值远大于一般出行的峰值,甚至多分布于20~40 km之间。总之,本文所提出的换乘行为提取算法能够实现城市换乘行为的精确提取,结合对不同换乘行为模式的有效分析,为城市交通、城市活力、公共交通设施和城市规划等方面的研究提供有效的模型算法支撑。

With the expansion of urban areas,a mix of transportation modes has become prevalent during the daily commutes of city dwellers.That is,commuters often need to transfer between various modes to reach their destinations.Accurate identification and analysis of these transfer behaviors are crucial for advancing urban transportation research.Current research tends to focus on distance or time thresholds,typically derived from walking speeds or anecdotal experience.However,these approaches often overlook the distinct station densities within cities.Other studies,while utilizing GPS,GTFS,and similar datasets,construct intricate transfer identification methods that lack generalizability.Against this backdrop,we introduce a time-distance dualconstraint transfer recognition algorithm.Firstly,leveraging extensive traffic IC card data,based on the statistical characteristics of the proximity distance sequences between bus or subway stations and their M neighboring stations,distance thresholds for bus-bus,bus-subway,and subway-bus transfer are detected individually.Subsequently,a filtering algorithm based on these distance thresholds is applied to daily data to produce a candidate transfer data set.Based on this,four time thresholds for each day are determined by analyzing the statistical characteristics of the transit time differences within the datasets.Finally,these dual thresholds facilitate the precise extraction of transfer behaviors.Furthermore,we establish a classification framework for these behaviors,classifying them into nine distinct transfer modes.These modes are defined based on the duration of travel time in the first and second journeys,encompassing variations including long-long,long-medium,longshort,middle-long,middle-middle,middle-short,short-long,short-middle,and short-short.We analyze these models individually for their travel characteristics.Results reveal that the morning peak for all transfer trips precedes that of buses and subways,with short-long transfers leading by up to 30 minutes.This underscores the added effort required by commuters who rely on transfers.In contrast,evening peak times vary,with certain transfer modes like long-long and long-short lagging notably behind the general evening peak.This further emphasizes the increased commuting burden associated with transfers.In terms of travel distances,the peak of regular subway travel distances is around 10 km,while that of the bus travel distances is around 1 km.The peak commuting distances for all nine transfer behaviors are greater than those of typical trips and are distributed within a range of 20~40 km.In summary,our method for extracting and analyzing transfer behaviors offers a robust and effective tool for urban transportation research,urban vitality assessment,public transportation planning,and urban planning.