Load Balancing for Mobility-on-Demand Systems

Abstract

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.We develop a rebalancing policy that minimizes the number of vehicles performing rebalancing trips. To do this, we utilize a fluid model for the customers and vehicles in the system. The model takes the form of a set of nonlinear time-delay differential equations. We then show that the optimal rebalancing policy can be found as the solution to a linear program. By analyzing the dynamical system model, we show that every station reaches an equilibrium in which there are excess vehicles and no waiting customers. 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 with stochastic features found in real-world urban transportation networks.

Simulation Framework for Rebalancing of Autonomous Mobility on Demand Systems

“…2). As proven in [9] this is equivalent to minimizing the rebalancing effort. This policy requires a priori knowledge of the demand d i j (t).…”

“…Some studies attempt to estimate the fleet size of autonomous shared-use vehicles [3][4][5][9][10][11].…”

“…The review is built upon work discussed in [5,[11][12][13][14]. [3,4] is published in continuance of [2,5,9,11]. These two studies make use of SimMobility, a microscopic simulation tool, which allows mode choice and congestion to be taken into account while performing evaluation of the AMOD system.…”

“…The offline rebalancing model developed in this study extends the linear program introduced in [9]. The objective of our model is to find the minimum number of vehicles at each station at the beginning of the day (Fig.…”

“…The online model used in this study is based on the fluid model first introduced in [5,9]. The model repeatedly solves the optimization formulated in [4] and it finds the rebalancing counts to match the anticipated demand at all stations.…”