Dynamic ridesharing has gained significant attention in recent years. However, existing ridesharing studies often focus on optimizing order dispatching and vehicle repositioning separately, leading to short-sighted decisions and underutilization of the ridesharing potential. In this paper, we propose a novel joint optimization framework called$\mathtt {JODR}$. By coordinating order dispatching and vehicle repositioning,$\mathtt {JODR}$enhances ridesharing efficiency while ensuring high-quality service. The core idea of$\mathtt {JODR}$is to dispatch ride orders with high demand in specific mobility directions to vehicles with sufficient available capacity, effectively balancing future supply and demand in those directions. To achieve this, we introduce a novel mobility value function that can predict the long-term mobility value of matching an order with its travel direction. By considering orders’ directional mobility values, service quality assessments, and available vehicle capacities,$\mathtt {JODR}$formulates the order dispatching as a minimum-cost maximum-flow problem to derive the optimal order-vehicle assignments. Furthermore, the value function helps the intelligent repositioning of idle vehicles. Extensive experiments conducted on a large real-world dataset demonstrate the superiority of$\mathtt {JODR}$over state-of-the-art methods across various performance metrics. These experimental results validate the effectiveness of$\mathtt {JODR}$in improving the ridesharing efficiency and experience.

Ride-hailing service (RHS) has become an important transportation mode in our daily life. Although many works have been proposed to improve RHS from different aspects, only few works focus on the selections of pick-up locations, where rider and driver meet and start a trip. In this paper, we presentMPLRec, a data-driven pick-up location recommendation system that exploits riders' specific mobility demands,e.g.destination, and historical experiences to meet riders' travel requirements.MPLRecgenerates potential pick-up locations over the road network and characterizes them with rich features that describe a location from the riders' perspective. We also build spatio-temporal indexes to organize potential pick-up locations and historical data for facilitating online recommending. When processing an online recommendation request,MPLRecderives candidate pick-up locations and investigates them with materialized features, which are computed from historical order and trajectory data while considering rider's mobility demands. Based on these features, a novel scoring function is used to derive the best pick-up location for each request. Moreover, we implement an RHS simulator to evaluateMPLRecusing large-scale practical ride-hailing datasets. Extensive experiments and simulations demonstrate the effectiveness and efficiency ofMPLRec, which can complete each request within 0.5 s and largely reduce the ride-hailing costs when compared to baseline methods.