Spatiotemporal Heterogeneity Analysis of Influence Factor on Urban Rail Transit Station Ridership

Jianpo Wang · Na Zhang · Hui Peng · Yan Huang

29 citations0/0 files storedJournal of Transportation Engineering Part A Systems

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Urban Transport and Accessibility Transportation Planning and Optimization Human Mobility and Location-Based Analysis

Abstract

Urban rail transit has effectively alleviated the pressure on road traffic. To explore the key influence factors that a rail station’s built environment has on passenger flow and its heterogeneity along with temporal and spatial changes, in this paper, a geographically and temporally weighted regression (GTWR) model was constructed to identify. Specifically, an empirical study was conducted in Xi’an, China, using 1 month of smartcard and station-level point-of-interest data. Firstly, we extracted an influence factors set (IFS) for ridership at the station level, and thereby three aspects of characteristics were obtained to establish IFS, including land usage, interchange connection facilities, and attributes for the station. Then, variables were exactly determined to describe each aspect characteristic with the analysis of the multicollinearity and spatial self-correlation. In addition, for models, ordinary least squares (OLS), geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR) were built to explore variables’ heterogeneity and variation influence in spatiotemporal for station ridership over time and location. Results reveal that GTWR outperforms in effectively capturing the spatiotemporal performance of ridership influences. Moreover, we proposed a mixture Poisson model to cluster stations with typical land-use characteristics in order of GTWRs’ application in different types of stations, for practice. In sum, ridership changes of different stations affected by a specific influences over time were analyzed, which highlighted the importance of temporal features in spatiotemporal data. Using GTWR to explore the relationship between ridership and station environment can provide insightful essential information for policymaking in urban rail transit management.

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References

1997Travel demand and the 3Ds: Density, diversity, and design 2010Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices 2015Geographical and Temporal Weighted Regression (GTWR)2004Factors influencing light-rail station boardings in the United States 2008Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas 2008How Far, by Which Route and Why? A Spatial Analysis of Pedestrian Preference 2017Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level

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