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Ma, T., Zhao, J., Xiang, S., Zhu, Y., & Liu, P. (2014). An agent-based training system for optimizing the layout of AFVs' initial filling stations. Journal of Artificial Societies and Social Simulation 17 (4) p. 6.
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An agent-based training system for optimizing the layout of AFVs' initial filling stations.pdf - Published Version Available under License Creative Commons Attribution. Download (506kB) | Preview |
Abstract
The availability of refuelling locations for alternative fuel vehicles (AFVs) is an important factor that drivers consider before adopting an AFV; thus, the layout of initial filling stations for AFVs will influence the adoption of AFVs. This paper presents a training system for optimising the layout of initial filling stations for AFVs by linking an agent-based model of the adoption of AFVs with a real city/area's road network, as well as the city/area's social and economic background. In the agent-based model, two types of agents (driver agents and station owner agents) interact with each other in a city/area's road network, stored in a GIS (Geographic Information System). With simulation scenario analyses and a genetic algorithm, the training system presented in this paper can help decision makers determine close-to-optimal layouts for initial AFV filling stations. This paper also presents case study of the application of the training system that analyses the layout of fast-charging or battery-changing stations for the promotion of electric vehicles adoption in Shanghai.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Training System, Optimal Layout, Alternative Fuel Vehicles, Filling Stations |
| Research Programs: | Transitions to New Technologies (TNT) |
| Depositing User: | IIASA Import |
| Date Deposited: | 15 Jan 2016 08:50 |
| Last Modified: | 27 Aug 2021 17:39 |
| URI: | https://pure.iiasa.ac.at/10766 |
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