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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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Title
The Charging Infrastructure Design Problem with Electric Taxi Demand Prediction Using Convolutional LSTM
Author
Hwang, Seong WookLim, Sunghoon
Issue Date
2022-04
Publisher
Inderscience Publishers
Citation
EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING, v.16, no.6, pp.1 - 1
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.
URI
https://scholarworks.unist.ac.kr/handle/201301/57731
DOI
10.1504/ejie.2022.10043497
ISSN
1751-5254
Appears in Collections:
SME_Journal Papers
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