Identifying the collaborative scheduling areas between ride-hailing and traditional taxi services based on vehicle trajectory data

Traditional taxi services (TTSs) play an important role in satisfying daily travel demands. The rapid growth of ride-hailing services (RHSs) has increased the convenience of customized travel. However, the volume of empty ride-hailing vehicles has increased. Identifying the collaborative scheduling areas (CoSAs) between RHSs and TTSs can further improve the efficiency of urban travel services. Therefore, we propose a method to identify CoSAs based on the travel demand and vehicle supply of TTSs and RHSs derived from trajectory data. We first optimize and make the temporal resolution of the trajectories of different types of vehicles uniform based on the shortest path algorithm. Then, the indicators describing travel demand and vehicle supply are defined and calculated. Finally, the areas with a high vehicle supply of one type and a low vehicle supply and high travel demand of the other type are identified as CoSAs. A dataset for Xiamen Island indicates that the CoSAs of TTSs to RHSs can provide potential routes to pick up passengers that are 41% and 11% shorter than the actual routes at 9:00 and 18:00, respectively. The constructed method can also improve the CoSA identification results.