Inferring Spatiotemporal Mobility Patterns from Multidimensional Trip Data

The massive amount of data related to spatiotemporal mobility offers new opportunities to understand human behaviors. However, with the increase of volume and complexity of mobility data, it has become challenging to retrieve important information and critical features of spatiotemporal mobility. In particular, predicting large-scale travel demands is challenging and requires a high computational load. This paper introduces a data-driven approach for estimating high-dimensional travel demands. We propose a method to identify mobility patterns using a probabilistic tensor decomposition approach for interpreting the complexity and uncertainty of mobility data. Expectation-maximization (EM) algorithm is applied for inferring mobility patterns. A case study is presented, where the proposed model is applied to New York city taxi data. The results show the model performance according to the number of origin and destination patterns and the number of trip data used. The probabilistic modeling results provide a deeper understanding of large-scale mobility data in the spatiotemporal dimension.