Ridership prediction and anomaly detection in transportation hubs: an application to New York City

Ridership modeling is a growing field critical for Intelligent Transportation. Accurate traffic prediction and early surge detection are vital components in designing public transit dispatching systems. However, modeling Spatio-temporal traffic at a small geographic scale and fine time granularity is challenging due to the sparseness, low signal-to-noise ratio, and the large dimensionality of the mobility network data. We propose a framework for edge-level traffic prediction to tackle these challenges, which addresses the curse of dimensionality through a pipeline of appropriate network aggregation, nonlinear modeling, and final edge-level disaggregation. Subsequently, we show that the low-dimensional aggregated space model residuals are more suited for anomaly detection than raw ridership data. Our framework is evaluated using the for-hire vehicle and taxi ridership dataset from the two airports in New York City, experimenting with different network aggregation techniques and modeling paradigms. The results reinstate the superiority of the proposed pipeline in ridership prediction and anomaly detection compared with single-model methods, and help build up scenario design for transportation simulation and planning.