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Lim, Sunghoon
Industrial Intelligence Laboratory
Research Interests
  • Industrial Artificial Intelligence (AI+X), Smart Manufacturing, Smart Factory

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The Charging Infrastructure Design Problem with Electric Taxi Demand Prediction Using Convolutional LSTM

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dc.contributor.author Hwang, Seong Wook ko
dc.contributor.author Lim, Sunghoon ko
dc.date.available 2022-04-07T23:12:23Z -
dc.date.created 2022-04-06 ko
dc.date.issued 2022-04 ko
dc.identifier.citation EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING, v.16, no.6, pp.1 - 1 ko
dc.identifier.issn 1751-5254 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/57731 -
dc.description.abstract The authors present a charging infrastructure design problem with electric taxi demand prediction. Due to environmental concerns, electric vehicle adoption has significantly increased in the transportation sector. However, the use of electric vehicles is not highly commercialised in the taxi industry, because the immature charging network and frequent charging decrease taxi revenue efficiency. Therefore, charging infrastructure needs to be built in urban areas in consideration of operational requirements of the taxi industry. The authors first design a convolutional long short-term memory model that predicts taxi demand, along with hotspots. Then, based on the predicted taxi demand in hotspots, a mixed integer linear programming model is proposed to optimise the location of recharging stations to minimise the cost of locating stations and charging service. Also, we propose a heuristic algorithm to solve realistic and practical problems. Lastly, a case study is presented to validate the proposed research. ko
dc.language 영어 ko
dc.publisher Inderscience Publishers ko
dc.title The Charging Infrastructure Design Problem with Electric Taxi Demand Prediction Using Convolutional LSTM ko
dc.type ARTICLE ko
dc.identifier.wosid 000877705800001 ko
dc.type.rims ART ko
dc.identifier.doi 10.1504/ejie.2022.10043497 ko
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