A Probabilistic Approach for Demand-Aware Ride-Sharing Optimization

Abstract

Ride-sharing is a modern urban-mobility paradigm with tremendous potential in reducing congestion and pollution. Demand-aware design is a promising avenue for addressing a critical challenge in ride-sharing systems, namely joint optimization of request-vehicle assignment and routing for a fleet of vehicles. In this paper, we develop a probabilistic demand-aware framework to tackle the challenge. We focus on maximizing the expected number of passenger pickups, given the probability distributions of future demands. The key idea of our approach is to assign requests to vehicles in a probabilistic manner. It differentiates our work from existing ones and allows us to explore a richer design space to tackle the requestvehicle assignment puzzle with a performance guarantee but still keeping the final solution practically implementable. The optimization problem is non-convex, combinatorial, and NP-hard in nature. As a key contribution, we explore the problem structure and propose an elegant approximation of the objective function to develop a dual-subgradient heuristic. We characterize a condition under which the heuristic generates a (1 − 1/e) approximation solution. Our solution is simple and scalable, amendable for practical implementation. Results of numerical experiments based on real-world traces in Manhattan show that, as compared to a conventional demand-oblivious scheme, our demand-aware solution improves the passenger pickups by up to 46%. The results also show that joint optimization at the fleet level leads to 19% more pickups than that by separate optimizations at individual vehicles.

Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems

“…Online Matching. Another set of related works [1,[30][31][32][33][34][35][36][37]] study online matching between orders and vehicles in the ridesharing systems. Cheng et al [31] and Miao et al [36] both take the future demand distribution into consideration in online matching without considering order cancellations.…”

Trip-vehicle matching and vehicle routing optimization for ride-sharing

“…Lin et al (2019), another route planning problem was formulated to maximize the expected number of riders picked up by multiple vehicles, which was solved by a (1 − 1/e) approximation algorithm based on dual sub-gradient decent.In real-time ride-sharing, some studies have adopted model-based learning techniques based on convolutional neural networkAl-Abbasi et al (2019) and long short-term memoryYao et al (2018) to predict future information on both driver and rider sides. Some other studies focus on vehicles dispatching and routing.…”