An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership
mode - subway/metro, place - asia, place - urban, ridership - demand, ridership - modelling, planning - methods
Geographically weighted regression, LASSO, Ada-GWL, Network-based distance metric, Subway ridership, Influencing factors
Ridership prediction at station level plays a critical role in subway transportation planning. Among various existing ridership prediction methods, direct demand model has been recognized as an effective approach. However, direct demand models including geographically weighted regression (GWR) have rarely been studied for local model selection in ridership prediction. In practice, acquiring insights into subway ridership under multiple influencing factors from a local perspective is important for passenger flow management and transportation planning operations adapting to local conditions. In this study, we propose an adapted geographically weighted LASSO (Ada-GWL) framework for modelling subway ridership, which involves regression-coefficient shrinkage and local model selection. It takes subway network layout into account and adopts network-based distance metric instead of Euclidean-based distance metric, making it so-called adapted to the context of subway networks. The real-world case of Shenzhen Metro is used to elaborate our proposed model. The results show that the proposed Ada-GWL model performs the best compared with the global model (ordinary least square, GWR, GWR calibrated with network-based distance metric and geographically weighted LASSO (GWL) in terms of estimation error and goodness-of-fit. Through understanding the variation of each coefficient across space (elasticities) and variables selection of each station, it provides more realistic conclusions based on local analysis. Besides, through clustering analysis of the stations according to the regression coefficients, clusters’ functional characteristics are found to be in compliance with the policy of functional land use in Shenzhen, indicating the high interpretability of Ada-GWL model from the spatial angle. In other words, the regression coefficients of different stations can provide us the local prospective to understand the influence of factors on stations’ ridership.
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He, Y., Zhao, Y. & Tsui, K.L. (2021). An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership. Transportation, Vol. 48, pp. 1185–1216.