Assessing urban-scale spatiotemporal heterogeneous metro station coverage using multi-source mobility data

Assessing the coverage of metro stations is crucial for evaluating and guiding metro construction. Existing methods mainly rely on surveys to obtain the coverage radii by fitting the first-mile distance distribution of metro passengers, which is costly and time-consuming to capture the spatiotemporal heterogeneity at the urban scale. Daily generated multi-source mobility data offers the possibility of a broad and low-cost assessment. This study proposes a framework to assess the coverage radius of metro stations using metro smart card data and Baidu population heatmap data. First, we build a nested logit model to model travelers' mode choice and station selection behaviors, considering both the competitiveness of the metro over other modes and travelers' sensitivity to first-mile distance. We then establish the relationship between choice probability and metro station inflows, calibrating the parameters through a genetic algorithm-based bi-objective optimization. Finally, we propose a novel metro station coverage assessment method using a distance-decay function that describes the cumulative mode choice proportions. An empirical analysis is conducted using Hangzhou, a sizeable monocentric city in China. The results reveal significant tidal patterns in travel behavior parameters. During the morning peak, suburban travelers rely more on the metro, whereas evening peak reliance is more pronounced among urban center travelers. This aligns with Hangzhou's commuting patterns. Moreover, significant differences occur in attraction patterns between downtown and suburban stations. Suburban metro stations exhibit larger coverage radii due to the lack of convenient alternative transport modes, a result that existing methods fail to capture. This evaluation framework can be extended to other cities, offering valuable insights for enhancing metro services.