Catchment-Area Delineation Approach Considering Travel Purposes for Station-Level Ridership Prediction Task

Chen Ma, Yanqiu Cheng, Shuang Zhang, Kuanmin Chen, Jie Wei, Xianbiao Hu

Research output: Contribution to journalArticlepeer-review

Abstract

Station-catchment-area delineation is a key component of direct ridership models for urban rail-transport systems as it can determine the relationship between the urban-rail-transit station-level ridership and the variables within the station catchment area. The neglect of differences in the passenger-flow distribution for different travel purposes in previous studies has led to low accuracy of the obtained walk-to-station distances. Therefore, this paper proposes a station-catchment-area delineation method which is based on web map data to obtain accurate walk-to-station distances and considers differences in the distance thresholds and the ridership attraction intensity (RAI) for six travel purposes (corresponding to commercial, medical, residential, educational, administrative, and recreational land uses). In the case study, the ridership data of Xi’an Metro, the 2015 Xi’an Residential Travel Survey data, and the corresponding Gaode Map data are employed to extract passengers’ walking-distance distribution for several travel purposes to delineate the station catchment areas and build direct ridership models. Several geographically weighted regression (GWR) models are constructed to evaluate and examine the effects of the various station-catchment-area delineation methods on the model findings. The obtained results show that the proposed station-catchment-area delineation method significantly improves the ridership prediction performance compared with the traditional circular-buffer method, with the entry and exit ridership prediction accuracy improving by 3.57% and 6.65% on average, respectively. Finally, this study will guide transportation planners on how to delineate station catchment areas when constructing direct-demand models for urban rail stations.

Original languageEnglish (US)
Pages (from-to)397-415
Number of pages19
JournalTransportation Research Record
Volume2678
Issue number5
DOIs
StatePublished - May 2024

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Mechanical Engineering

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