{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T17:01:23Z","timestamp":1781110883372,"version":"3.54.1"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,13]],"date-time":"2018-07-13T00:00:00Z","timestamp":1531440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Foundation of Jiangsu Province","award":["BK20151201"],"award-info":[{"award-number":["BK20151201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the depletion of oil resources and the aggravation of environmental pollution, electric vehicles have a growing future and will be more popular as the main force of new energy consumption. They have attracted greater attention from various countries. The sizing and location problem for charging stations has been a hot point of global research, and these issues are important for government planning for electric vehicles. In this paper, we first built a BASS model to predict the total number of electric vehicles and calculate the size of charging stations in the coming years. Moreover, we also developed a queuing model to optimize the location of charging stations and solved this issue by using the exhaustion method, which regards minimum cost as the objective function. After that, the model was tested using data from a city in China. The results show that the model in this paper is good at predicting the number of electric vehicles in the coming years and calculating the size of charging stations. At the same time, it can also optimize the distribution of charging stations and make them more balanceable. Thus, this model is beneficial for the government in planning the development of electric vehicles in the future.<\/jats:p>","DOI":"10.3390\/info9070170","type":"journal-article","created":{"date-parts":[[2018,7,16]],"date-time":"2018-07-16T04:05:33Z","timestamp":1531713933000},"page":"170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Deploying Method for Predicting the Size and Optimizing the Location of an Electric Vehicle Charging Stations"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9072-6099","authenticated-orcid":false,"given":"Jian","family":"Ma","sequence":"first","affiliation":[{"name":"School of Civil Engineering, Suzhou University of Science and Technology, 1701 Binhe Road, New District, Suzhou 215011, China"},{"name":"Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Suzhou University of Science and Technology, 1701 Binhe Road, New District, Suzhou 215011, China"},{"name":"Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.3906\/elk-1311-221","article-title":"A new deployment method for electric vehicle charging infrastructure","volume":"24","author":"Yagcitekin","year":"2015","journal-title":"Turk. 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