Simulation Framework for Rebalancing of Autonomous Mobility on Demand Systems

Abstract

Abstract. We are observing a disruption in the urban transportation worldwide. The number of cities offering shared-use on-demand mobility services is increasing rapidly. They promise sustainable and affordable personal mobility without a burden of owning a vehicle. Despite growing popularity, on-demand services, such as carsharing, remain niche products due to small scale and rebalancing issues. We are proposing an extension to the traditional carsharing, which is Autonomous Mobility on Demand (AMOD). AMOD provides a one-way carsharing with self-driving electric vehicles. Autonomous vehicles can make the carsharing more attractive to customers as they (i) reduce the operating cost, which is incurred when a manually driven system is unbalanced, and (ii) release people from the burden of driving. This study is built upon our previous work on Autonomous Mobility on Demand (AMOD) systems. Our methodology is simulation-based and we make use of SimMobility, an agent-based microscopic simulation platform. In the current work we focus on the framework for testing different rebalancing policies for the AMOD systems. We compare three different rebalancing methods: (i) no rebalancing, (ii) offline rebalancing, and (iii) online rebalancing. Simulation results indicate that rebalancing reduces the required fleet size and shortens the customers' wait time.

Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems

“…Addressing the imbalanced demand patterns, the authors proposed several relocation strategies to balance vehicle supply and reduce future traveler wait times: (1) relocating vehicles based on expected demand and (2) relocating vehicles to balance stock based on predicted supply. Marczuk et al [112] developed a simulation framework for rebalancing a one-way SAVs system in SimMobility environment. The proposed fleet management center is responsible for passenger-to-vehicle assignment, vehicle routing and rebalancing.…”

Vehicle Rebalancing for Mobility-on-Demand Systems with Ride-Sharing

“…More relevant to this paper are rebalancing techniques for autonomous mobility-on-demand systems, a car sharing scheme in which vehicles are able to redistribute themselves without the intervention of a human operator. Much of this research shows that a significantly reduced fleet size is required to serve a fixed demand with a similar quality of service [4], [13]. However, these works often use long rebalancing intervals [13] or use simplified models to simulate demand and vehicle movement [4].…”

“…Much of this research shows that a significantly reduced fleet size is required to serve a fixed demand with a similar quality of service [4], [13]. However, these works often use long rebalancing intervals [13] or use simplified models to simulate demand and vehicle movement [4]. The work in [14] presents an model predictive controller to optimize vehicle scheduling and routing, however the experiments were conducted with a relatively small fleet (40 vehicles).…”

Value of demand information in autonomous mobility-on-demand systems

“…Powered by the autonomous vehicles (AV), the concept of shared autonomous mobility-on-demand (AMoD) systems is poised to be one of the most disruptive innovations in the transportation industry. Although AMoD applications remain niche products, research on better design and operation of such systems have led to system design evaluation [1,2,3,4,5,6], advanced dispatching algorithms [7,8,9], demand prediction tools [10,11] and dynamic pricing [12,13].…”