An Agent-Based Training System for Optimizing the Layout of AFVs' Initial Filling Stations

Authored by Tieju Ma, Ya Zhu, Peipei Liu, Jiangjiang Zhao, Shijian Xiang

Date Published: 2014-10-31

Sponsors: Chinese National Natural Science Foundation Ministry of Education of China Shanghai Science and Technology Committee Shanghai Education Committee

Platforms: No platforms listed

Model Documentation: ODD Flow charts Mathematical description

Model Code URLs: Model code not found

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 a 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.

Tags

Simulation models diffusion Filling Stations Optimal Layout Training System alternative fuel vehicles Vehicles Economy