Robotic load balancing for mobility-on-demand systems

Abstract

In this paper we develop methods for maximizing the throughput of a mobility-on-demand urban transportation system. We consider a finite group of shared vehicles, located at a set of stations. Users arrive at the stations, pick-up vehicles, and drive (or are driven) to their destination station where they drop-off the vehicle. When some origins and destinations are more popular than others, the system will inevitably become out of balance: Vehicles will build up at some stations, and become depleted at others. We propose a robotic solution to this rebalancing problem that involves empty robotic vehicles autonomously driving between stations. Specifically, we develop a rebalancing policy that lets every station reach an equilibrium in which there are excess vehicles and no waiting customers and that minimizes the number of robotic vehicles performing rebalancing trips. To do this, we utilize a fluid model for the customers and vehicles in the system. We then show that the optimal rebalancing policy can be found as the solution to a linear program. We use this solution to develop a real-time rebalancing policy which can operate in highly variable environments. We verify policy performance in a simulated mobility-on-demand environment and in hardware experiments.

Multi-Objective Analysis of Ridesharing in Automated Mobility-on-Demand

“…The early works in AMoD focused on the development of models and algorithmic tools for on-demand fleets with single-occupancy vehicles [6,14,17,20,15]. However, one of the main promises of on-demand systems is the ability to implement massive ridesharing.…”

Privacy-preserving vehicle assignment for mobility-on-demand systems

“…The total vehicle fleet size is variable throughout time -since the taxicab dataset contains records of occupied vehicles only, we make use of a heuristic to compute the total number of vehicles in the fleet (occupied plus available vehicles). This value is computed from the number of occupied rides obtained from the real taxi data, and is multiplied by 1.56 (which corresponds to a ratio of 64% of occupied taxis 11 ). We cap the maximum fleet size at 6000 vehicles.…”

“…However, we are unaware of its application to the MoD problem. Indeed, most literature in the domain of MoD systems focuses on the questions of load re-balancing and predictive positioning [9][10][11]15], and vehicle assignment with passenger pooling [2,13].…”

Markov-based redistribution policy model for future urban mobility networks

“…For a review of the state of the art see [2], [10] and the references therein. Autonomous load rebalancing in MOD systems has recently been studied in [11] and [12], where a fluid model was used to represent supply and demand. As well as system-level traffic flow optimizations, socially motivated criteria have also been considered.…”