Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models

The spatio-temporal variation in the demand for transportation, particularly taxis, in the highly dynamic urban space of a metropolis such as New York City is impacted by various factors such as commuting, weather, road work and closures, disruptions in transit services, etc. This study endeavors to explain the user demand for taxis through space and time by proposing a generalized spatio-temporal autoregressive (STAR) model. It deals with the high dimensionality of the model by proposing the use of LASSO-type penalized methods for tackling parameter estimation. The forecasting performance of the proposed models is measured using the out-of-sample mean squared prediction error (MSPE), and the proposed models are found to outperform other alternative models such as vector autoregressive (VAR) models. The proposed modeling framework has an easily interpretable parameter structure and is suitable for practical application by taxi operators. The efficiency of the proposed model also helps with model estimation in real-time applications.