Title: The charging infrastructure design problem with electric taxi demand prediction using convolutional LSTM

Authors: Seong Wook Hwang; Sunghoon Lim

Addresses: Department of Business Administration, Sungshin Women's University, Seoul, South Korea ' Department of Industrial Engineering and Institute for the 4th Industrial Revolution, Ulsan National Institute of Science and Technology, Ulsan, South Korea

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. [Submitted: 28 April 2021; Accepted: 5 September 2021]

Keywords: OR in service industries; transportation; heuristics; machine learning; artificial intelligence.

DOI: 10.1504/EJIE.2022.126633

European Journal of Industrial Engineering, 2022 Vol.16 No.6, pp.651 - 678

Received: 28 Apr 2021
Accepted: 05 Sep 2021

Published online: 31 Oct 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article