Spotting Trip Purposes from Taxi Trajectories

Abstract

What is the purpose of a trip? What are the unique human mobility patterns and spatial contexts in or near the pickup points and delivery points of trajectories for a specific trip purpose? Many prior studies have modeled human mobility patterns in urban regions; however, these analytics mainly focus on interpreting the semantic meanings of geographic topics at an aggregate level. Given the lack of information about human activities at pick-up and dropoff points, it is challenging to convert the prior studies into effective tools for inferring trip purposes. To address this challenge, in this article, we study large-scale taxi trajectories from an unsupervised perspective in light of the following observations. First, the POI configurations of origin and destination regions closely relate to the urban functionality of these regions and further indicate various human activities. Second, with respect to the functionality of neighborhood environments, trip purposes can be discerned from the transitions between regions with different functionality at particular time periods. Along these lines, we develop a general probabilistic framework for spotting trip purposes from massive taxi GPS trajectories. Specifically, we first augment the origin and destination regions of trajectories by attaching neighborhood POIs. Then, we introduce a latent factor, POI Topic , to represent the mixed functionality of the regions, such that each origin or destination point in the city can be modeled as a mixture over POI Topics. In addition, considering the transitions from origins to destinations at specific time periods, the trip time is generated collaboratively from the pairwise POI Topics at both ends of the O-D pairs, constituting POI Links , and hence the trip purpose can be explained semantically by the POI Links. Finally, we present extensive experiments with the real-world data of New York City to demonstrate the effectiveness of our proposed method for spotting trip purposes, and moreover, the model is validated to perform well in predicting the destinations and trip time among all the baseline methods.