Bus-Subway Interchange Mode Research with IC Card Data

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 dual-constraint 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, long-short, 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. © 2024 Science Press. All rights reserved.