| Issue |
MATEC Web Conf.
Volume 81, 2016
2016 5th International Conference on Transportation and Traffic Engineering (ICTTE 2016)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 6 | |
| Section | Transportation Planning and System Optimization | |
| DOI | https://doi.org/10.1051/matecconf/20168101005 | |
| Published online | 25 October 2016 | |
Simulation Framework for Rebalancing of Autonomous Mobility on Demand Systems
1 National University of Singapore
2 University of Toronto
3 Massachusetts Institute of Technology
4 Singapore-MIT Alliance for Research and Technology
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.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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