Simulation Framework for Rebalancing of Autonomous Mobility on Demand Systems
نویسندگان
چکیده
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 selfdriving 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.
منابع مشابه
Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems
The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-onDemand systems (AMoD, i.e. fleets of self-driving vehicles). We first model the AMoD system using a time-expanded network, and present a formulation that computes the optimal rebalancing strategy (i.e., preemptive repositioning) and the minimum feasible fleet size for a given travel demand...
متن کاملShared-vehicle Mobility-on-demand Systems: a Fleet Operator’s Guide to Rebalancing Empty Vehicles
1 We consider the operation of automated mobility-on-demand systems, whereby users share access to a fleet 2 of self-driving vehicles. In these systems, rebalancing, the process by which the supply of empty vehicles is 3 periodically realigned with the demand for transport, is carried out by a fleet operator. Where much of the 4 rebalancing literature skews to the theoretical or simulation-base...
متن کاملRouting Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms
This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not le...
متن کاملControl of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspective
In this paper we present queueing-theoretical methods for the modeling, analysis, and control of autonomous mobilityon-demand (MOD) systems wherein robotic, self-driving vehicles transport customers within an urban environment and rebalance themselves to ensure acceptable quality of service throughout the network. We first cast an autonomous MOD system within a closed Jackson network model with...
متن کاملA Multi-commodity Pickup and Delivery Open-tour m-TSP Formulation for Bike Sharing Rebalancing Problem
Bike sharing systems (BSSs) offer a mobility service whereby public bikes, located at different stations across an urban area, are available for shared use. An important point is that the distribution of rides between stations is not uniformly distributed and certain stations fill up or empty over time. These empty and full stations lead to demand for bikes and return boxes that cannot be fulfi...
متن کامل