Spatio-temporal Modeling of Yellow Taxi Demands in New York City Using Generalized STAR Models

A highly dynamic urban space in a metropolis such as New York City, the spatio-temporal variation in demand for transportation, particularly taxis, is impacted by various factors such as commuting, weather, road work and closures, disruption in transit services, etc. To understand the user demand for taxis through space and time, a generalized spatio-temporal autoregressive (STAR) model is proposed in this study. In order to deal with the high dimensionality of the model, LASSO-type penalized methods are proposed to tackle the parameter estimation. The forecasting performance of the proposed models is measured using the out-of-sample mean squared prediction error (MSPE), and it is found that the proposed models outperform other alternative models such as vector autoregressive (VAR) models. The proposed modeling framework has an easily interpretable parameter structure and practical to be applied by taxi operators. Efficiency of the proposed model also helps in model estimation in real-time applications.