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 work, 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 request-vehicle 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 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. The solution is simple and scalable, amendable for practical implementation. Simulation results based on real-world traces in Manhattan show that our demand-aware solution improves the passenger pickups by up to 46%, as compared to a conventional demand-oblivious scheme. The results also show that joint optimization at the fleet level leads to 19% more pickups than that by separate optimizations at individual vehicles.

Speaker: Professor Minghua CHEN
Date: 29 April 2020 (Wed)
Time: 11:00am - 12:00pm
Poster: Click here