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Sustainability 2019, 11(3), 643; https://doi.org/10.3390/su11030643

Planning of the Charging Station for Electric Vehicles Utilizing Cellular Signaling Data

1
School of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China
2
Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
3
Tianjin Urban Planning & Design Institute, Tianjin 300201, China
*
Author to whom correspondence should be addressed.
Received: 17 December 2018 / Revised: 22 January 2019 / Accepted: 23 January 2019 / Published: 26 January 2019
(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)
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Abstract

Electric Vehicles (EVs), by reducing the dependency on fossil fuel and minimizing the traffic-related pollutants emission, are considered as an effective component of a sustainable transportation system. However, the massive penetration of EVs brings a big challenge to the establishment of charging infrastructures. This paper presents the approach to locate charging stations utilizing the reconstructed EVs trajectory derived from the Cellular Signaling Data (CSD). Most previous work focused on the commute trips estimated from the number of jobs and households between traffic analysis zones (TAZs). This paper investigated the large-scale CSD and illustrated the method to generate the 24-hour travel demand for each EV. The complete trip in a day for EV was reconstructed through merging the time sequenced trajectory derived from simulation. This paper proposed a two-step model that grouped the charging demand location into clusters and then identified the charging station site through optimization. The proposed approach was applied to investigate the charging behavior of medium-range EVs with Cellular Signaling Data collected from the China Unicom in Tianjin. The results indicate that over 50% of the charging stations are located within the central urban area. The developed approach could contribute to the planning of future charging stations. View Full-Text
Keywords: electric vehicle; cellular signaling data; reconstructed trajectory; charging station layout; clustering analysis electric vehicle; cellular signaling data; reconstructed trajectory; charging station layout; clustering analysis
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Jia, J.; Liu, C.; Wan, T. Planning of the Charging Station for Electric Vehicles Utilizing Cellular Signaling Data. Sustainability 2019, 11, 643.

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