10766 7 70970 2 196572 document 70970 An agent-based training system for optimizing the layout of AFVs' initial filling stations.pdf application/pdf 91e12bccef71920ba4c0741a604b3b83 MD5 506359 2016-09-14 07:37:12 https://pure.iiasa.ac.at/id/eprint/10766/1/An%20agent-based%20training%20system%20for%20optimizing%20the%20layout%20of%20AFVs%27%20initial%20filling%20stations.pdf 10766 1 1 application/pdf text en public cc_by

An agent-based training system for optimizing the layout of AFVs' initial filling stations.pdf

published 70971 2 196577 document 70971 lightbox.jpg image/png 2af79182fc9eacaac496b68fc15fb961 MD5 55934 2016-09-14 07:47:42 https://pure.iiasa.ac.at/id/eprint/10766/2/lightbox.jpg 10766 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/70970 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/70970 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/70970 70975 1 196581 document 70975 indexcodes.txt text/x-c++ 79580cd3c04d0416c8aa76ab3cca7c27 MD5 10559 2016-09-14 07:47:46 https://pure.iiasa.ac.at/id/eprint/10766/6/indexcodes.txt 10766 6 6 text/x-c++ other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/70970 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/70970 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/70970 archive 351 disk0/00/01/07/66 2016-01-15 08:50:08 2021-08-27 17:39:36 2016-01-15 08:50:08 article show Ma T. 1804 Zhao J. Xiang S. Zhu Y. Liu P. pub iis_mod prog_tnt Training System, Optimal Layout, Alternative Fuel Vehicles, Filling Stations 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. 2014-10 published SimSoc Consortium http://jasss.soc.surrey.ac.uk/17/4/6.html XJ-14-286 <jasss.soc.surrey.ac.uk/17/4/6.html> 191 public Journal of Artificial Societies and Social Simulation 17 4 6 6 TRUE 1460-7425 FALSE info:eu-repo/semantics/article