Geographically Modeling and Understanding Factors Influencing Transit Ridership: An Empirical Study of Shenzhen Metro

Abstract

Ridership analysis at the local level has a pivotal role in sustainable urban construction and transportation planning. In practice, urban rail transit (URT) ridership is affected by complex factors that vary across the urban area. The aim of this study is to model and explore the factors that impact metro station ridership in Shenzhen, China from a local perspective. The direct demand model, which uses ordinary least squares (OLS) estimation, is the most widely used method of ridership modeling. However, OLS estimation assumes parametric stability. This study investigates the use of a direct demand model on the basis of geographically weighted regression (GWR) to model the local relationships between metro station ridership and potential influencing factors. Real-world Shenzhen Metro smart card data are used to test and verify the applicability and performance of the model. The results show that GWR performs better than OLS estimation in terms of both model fitting and spatial interpretation. The GWR model demonstrates a high level of interpretability regarding the spatial distribution and variation of each coefficient, and thus can provide insights for decision-makers into URT ridership and its complex factors from a local perspective.

Visualization in Operations Management Research

“…Being able to overlay a range of related details while leveraging proximal and holistic structures of a system exemplifies the interpretive strengths of heat map depictions, which continue to motivate their use in various OM settings as well as in other disciplinary contexts (cf. He et al 2019He et al , 2020Konitzer et al 2019). In short, visualization in this instance and others provides a window into the enhancement and predictive abilities of the theoretical models under consideration.…”

Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow

“…All of the POI data within a PCA were collected from Baidu Map with the assistance of API, and POI data consist of the stations' nearby residence, entertainment, services, business, education, offices. Specifically, the information covers the numbers of residences, restaurants, schools, offices, hospitals, banks, shopping places, bus stations, and hotels within 500m PCA [55].…”

Transit Usage in Social Shocks: A Case Study of Station-Level Metro Ridership in Anti-Extradition Protests in Hong Kong

“…Interchange stations are more desirable to passengers than intermediate stations, and they appear to draw more riders, whereas intermodal stations, which accept riders from other modes of transportation, have higher boarding rates. The centrality of stations within the network is also important because people use public transportation more often in central areas than in peripheral areas ( 33 , 34 ).…”

“…They estimate ridership as a function of station environments and transit service features, using multiple regression because of its ability to simultaneously evaluate the effects of a bunch of selected factors. Multiple regression is flexible, widely used and easily understood by a broad audience ( 23 , 24 , 28 , 29 , 32 – 34 ). In general, most studies used transit stations as the unit of analysis, and the resulting model can be used for predictive purposes.…”