Evaluation of Electric Vehicle Charging Station Accessibility Based on an Improved Gravity Model

DOI: https://doi.org/10.62517/jcte.202606209

Author(s)

Yuan Xiao

Affiliation(s)

Wuhan University of Technology, Wuhan, Hubei, China *Corresponding Author

Abstract

The accessibility of charging infrastructure is a core factor influencing the development of electric vehicles. Evaluations based on traditional gravity models often exhibit limitations: first, the quantitative indicators for supply point service capacity are overly simplistic, failing to reflect variations among different charging stations; second, they neglect spatial competition effects between demand points and users' utility-maximizing choice behaviors, leading to discrepancies between evaluation results and actual conditions.To address these shortcomings, this study proposes a multidimensional evaluation system for charging station service capacity. Key indicators such as the number of charging piles and charging power are selected, with weights determined using the entropy weight method to accurately quantify the comprehensive service capacity of each charging station.Second, a maximum utility function is introduced to define the competitive service range of charging stations by calculating user choice probabilities under varying travel costs. This improves the traditional gravity model's method for delineating supply point service areas and is applied to assess the accessibility of electric vehicle charging stations in Guangzhou's central urban districts.The evaluation results indicate: (1) The spatial pattern of electric vehicle accessibility in Guangzhou's central urban area is imbalanced, exhibiting strong spatial polarization. Over 59.1% of streets have charging station accessibility below the citywide average,while only a few streets exhibit high accessibility. (2) A clear positive spatial correlation exists among charging facility accessibility levels in central Guangzhou. Influenced by regional functions and population distribution, distinct "high-high" (H-H) and "low-low" (L-L) agglomeration zones are evident, with a notable absence of "low-high" (L-H) and "high-low" (H-L) agglomeration zones.

Keywords

Electric Vehicle Charging Stations; Accessibility; Gravity Model; Guangzhou

References

[1] Wang,H., Huang,Q., Zhang, C.H. (2010) A novel approach for the layout of electric vehicle charging station. In: The 2010 International Conference on Apperceiving Computing and Intelligence Analysis Proceeding. IEEE, pp 64–70 [2] Huang, K., Kanaroglou, P., Zhang, X. (2016) The design of electric vehicle charging network. Transportation Research Part D: Transport and Environment, 49:1–17. [3] Liu, Z., Wen, F., Ledwich, G. (2013) Optimal Planning of Electric-Vehicle Charging Stations in Distribution Systems. IEEE Trans Power Delivery, 28:102–110. [4] Davidov, S. (2020) Optimal charging infrastructure planning based on a charging convenience buffer. Energy, 192:116655. [5] Çelik, S., Ok, Ş. (2024) Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning. Heliyon, 10:e29153. [6] Ren,Y.L. (2011) Optimal Distribution and Scale of Charging Stations for Electric Vehicles.Automation of Electric Power Systems, 35:53-57 [7] Kłos, M.J., Sierpiński, G. (2023) Siting of electric vehicle charging stations method addressing area potential and increasing their accessibility. Journal of Transport Geography,109:103601. [8] Falchetta, G., Noussan, M. (2021) Electric vehicle charging network in Europe: An accessibility and deployment trends analysis. Transportation Research Part D: Transport and Environment, 94:102813. [9] Sheng, J., Xiang, Z., Ban, P., Bao, C. (2024) How Does the Urban Built Environment Affect the Accessibility of Public Electric-Vehicle Charging Stations? A Perspective on Spatial Heterogeneity and a Non-Linear Relationship. Sustainability, 17:86. [10] Hansen, W.G. (1959) How Accessibility Shapes Land Use. Journal of the American Institute of Planners, 25:73–76. [11] Luo, W., Wang, F. (2003) Measures of Spatial Accessibility to Health Care in a GIS Environment: Synthesis and a Case Study in the Chicago Region. Environ Plann B Plann Des, 30:865–884. [12] Frade, I., Ribeiro, A., Gonçalves, G., Antunes, A.P. (2011) Optimal Location of Charging Stations for Electric Vehicles in a Neighborhood in Lisbon, Portugal. Transportation Research Record: Journal of the Transportation Research Board, 2252:91–98. [13] Sun, M., Gao, C., Xue, C., et al. (2021) A Data-Driven Method for Measuring Accessibility to Healthcare Using the Spatial Interpolation Model. IEEE Access, 9:64972–64982. [14] Du, X., Du, Y., Zhang, Y., et al. (2024) Urban and rural disparities in general hospital accessibility within a Chinese metropolis. Sci Rep, 14:23359. [15] Esmaili ,A., Oshanreh, M.M., Naderian, S., et al. (2024) Assessing the spatial distributions of public electric vehicle charging stations with emphasis on equity considerations in King County, Washington. Sustainable Cities and Society, 107:105409. [16] Anselin, L. (1995) Local Indicators of Spatial Association-LISA. Geographical Analysis, 27:93–115. [17] Tu, W. (2015) Quantitative Methods and Socio-Economic Applications in GIS. The AAG Review of Books, 3:135–137. [18] Gentleman,R., Hornik,K., Parmigian,G. (2008) Applied Spatial Data Analysis with R. Springer New York, New York, NY [19] Lyu, S., Pan, Y., Miao, H., et al. (2024) Comprehensive Evaluation Method for Electric Vehicle Charging Network Service Quality Considering User Satisfaction. WEVJ, 16:17. [20] Li, Q., Xiao, Y., Zhao, S., et al. (2019) Performance Status Evaluation of an Electric Vehicle Charging Infrastructure Based on the Fuzzy Comprehensive Evaluation Method. WEVJ, 10:35. [21] Roszkowska, E., Wachowicz, T. (2024) Impact of Normalization on Entropy-Based Weights in Hellwig’s Method: A Case Study on Evaluating Sustainable Development in the Education Area. Entropy, 26:365. [22] Qu, W., Li, J., Song, W., et al. (2022) Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction. Entropy, 24:849. [23] Jianyuan, W., Chengcheng, G., Kechen, L. (2022) Anomaly electricity detection method based on entropy weight method and isolated forest algorithm. Front Energy Res, 10:984473.