Understanding the land use function of station areas based on spatiotemporal similarity in rail transit ridership: A case study in Shanghai, China

In recent decades, transit-oriented development (TOD) has been considered as an effective way to alleviate the negative impacts of rapid urbanization. The ridership of rail stations is an important indicator for understanding the relationship between rail transit and land use, which helps enhance their coordinated development. How to regulate such development is a fundamental question that planning of rail stations and surrounding land use endeavors to answer. Understanding the relationship between rail transit and land use is the basis of effective planning. From smart card data (SCD), the boarding and alighting ridership time series can be extracted to understand their relationship from a new perspective. Current studies mainly consider the temporal similarity of ridership time series between individual stations but ignore spatial dependency between a station and its adjacent stations. To fill this gap, this paper proposes a novel method for refining the understanding from a spatiotemporal similarity perspective. First, we measure the temporal similarity of ridership time series between two individual stations using the autocorrelation function method. Second, we determine the spatiotemporal similarity between two stations by integrating the temporal similarity from their adjacent stations based on spatial dependency. Finally, the extracted spatiotemporal similarity is used for spectral clustering. As a case study, we analyze SCD on five consecutive weekdays at 289 rail stations in Shanghai, China. The land use functions of rail station areas are classified into 6 clusters: employment-oriented stations, residential-oriented stations, mixed stations, etc. By comparing the enrichment factors of each type of points of interest (POIs) in these clusters, the dominant land use function between our classification and POI information is demonstrated to be consistent. Furthermore, using three clustering performance indexes and several typical stations, the priority of our proposed method with integration of the spatial dependency can be confirmed to be superior to those without considering such important information. We conclude that the proposed method and findings are beneficial to balancing the needs of public transportation development and promoting the integration of transportation and land use for TOD implementation.