Learning ride-sourcing drivers’ customer-searching behavior: A dynamic discrete choice approach

Abstract Ride-sourcing drivers spend a significant portion of their service time being idle, during which they can move freely to search for the next customer. Such customer-searching movements, while not being directly controlled by ride-sourcing platforms, impose great impacts on the service efficiency of ride-sourcing systems and thus need to be better understood. To this purpose, we design a dynamic discrete choice framework by modeling drivers’ customer search as absorbing Markov decision processes. The model enables us to differentiate three latent search movements of idle drivers, as they either remain motionless, cruise around without a target area, or reposition toward specific destinations. Our calibration takes advantage of large-scale empirical datasets from Didi Chuxing, including the transaction information of five million passenger requests and the trajectories of 32,000 affiliated drivers. The calibration results uncover the variations of drivers’ attitudes in customer search across time and space. In general, ride-sourcing drivers do respond actively and positively to the repetitive market variations when idle. They are comparatively more mobile at high-demand hotspots while preferring to stay motionless in areas with long time of waiting being expected. Our results also suggest that drivers’ search movements are not confined to local considerations. Instead, idle drivers show a clear tendency of repositioning toward the faraway hotspots, especially during the evening when the demand cools down in the suburb. The discrepancies between full-time and part-time drivers’ search behavior are also examined quantitatively.