Measuring exposure and contribution of different types of activity travels to traffic congestion using GPS trajectory data

This study proposes a data-driven framework for understanding the space-time patterns of exposure and contribution of different activities to traffic congestion in urban road networks by using GPS trajectory and Pointof-Interest (POI) big datasets. Taking taxi trips related to traffic congestion in Wuhan, China as a case study, we first infer the types of individual activities from GPS trajectories and POIs and identify traffic congestion on each road. Then we develop two indicators to measure the congestion exposure of different activity types. Further, we reveal the space-time patterns of activity-related congestion through spatiotemporal analysis of the indicators of traffic congestion associated with different activities. The findings of this study shed light on how different types of activities contribute to the space-time heterogeneity of traffic congestion, and highlight the significance of considering the space-time patterns of congestion related with different activity types in urban transportation management.