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Open AccessArticle
Optimizing Station Placement for Free-Floating Electric Vehicle Sharing Systems: Leveraging Predicted User Spatial Distribution from Points of Interest
by
Qi Cao
Qi Cao
,
Shunchao Wang
Shunchao Wang *,
Bingtong Wang
Bingtong Wang and
Jingfeng Ma
Jingfeng Ma
School of Transportation, Southeast University, Nanjing, 211189, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2024, 13(7), 233; https://doi.org/10.3390/ijgi13070233 (registering DOI)
Submission received: 25 March 2024
/
Revised: 18 June 2024
/
Accepted: 25 June 2024
/
Published: 1 July 2024
Abstract
Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed in high-demand areas to reduce the scheduling costs. However, the planning of the FFEVS system includes a series of aspects of rental stations and charging stations, such as the location, size, and number, which interact with each other. In this paper, we first provide a method for forecasting the demand for car sharing based on the land characteristics of Beijing FFEVS station catchment areas. Then, the multi-objective MILP model for planning FFEVS systems is developed, which considers the requirements of vehicle relocation and electric vehicle charging. Afterward, the capabilities of the proposed models are demonstrated by the real data obtained from Beijing, China. Finally, the sensitivity analysis of the model is made based on varying demand and subsidy levels. From the results, the proposed model can provide decision-makers with useful insights about the planning of FFEVS systems, which bring great benefits to formulating more rational policies.
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MDPI and ACS Style
Cao, Q.; Wang, S.; Wang, B.; Ma, J.
Optimizing Station Placement for Free-Floating Electric Vehicle Sharing Systems: Leveraging Predicted User Spatial Distribution from Points of Interest. ISPRS Int. J. Geo-Inf. 2024, 13, 233.
https://doi.org/10.3390/ijgi13070233
AMA Style
Cao Q, Wang S, Wang B, Ma J.
Optimizing Station Placement for Free-Floating Electric Vehicle Sharing Systems: Leveraging Predicted User Spatial Distribution from Points of Interest. ISPRS International Journal of Geo-Information. 2024; 13(7):233.
https://doi.org/10.3390/ijgi13070233
Chicago/Turabian Style
Cao, Qi, Shunchao Wang, Bingtong Wang, and Jingfeng Ma.
2024. "Optimizing Station Placement for Free-Floating Electric Vehicle Sharing Systems: Leveraging Predicted User Spatial Distribution from Points of Interest" ISPRS International Journal of Geo-Information 13, no. 7: 233.
https://doi.org/10.3390/ijgi13070233
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