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A highly dynamic urban space in a metropolis such as New York City, thespatio-temporal variation in demand for transportation, particularly taxis, isimpacted by various factors such as commuting, weather, road work and closures,disruption in transit services, etc. To understand the user demand for taxisthrough space and time, a generalized spatio-temporal autoregressive (STAR)model is proposed in this study. In order to deal with the high dimensionalityof the model, LASSO-type penalized methods are proposed to tackle the parameterestimation. The forecasting performance of the proposed models is measuredusing the out-of-sample mean squared prediction error (MSPE), and it is foundthat the proposed models outperform other alternative models such as vectorautoregressive (VAR) models. The proposed modeling framework has an easilyinterpretable parameter structure and practical to be applied by taxioperators. Efficiency of the proposed model also helps in model estimation inreal-time applications.

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