{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:35:54Z","timestamp":1760060154811,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T00:00:00Z","timestamp":1754265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hubei Provincial Natural Science Foundation Project\u2013Xiangyang innovation and development joint fund","award":["2025AFD064"],"award-info":[{"award-number":["2025AFD064"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due to low traffic flow, resulting in a waste of resources. Areas with high traffic flow may have fewer charging stations, resulting in long queues and road congestion. The purpose of this study is to optimize the location of charging stations and the number of charging piles in the stations based on the distribution of traffic flow, and to construct a bi-level programming model by analyzing the distribution of traffic flow. The upper-level planning model is the user-balanced flow allocation model, which is solved to obtain the optimal traffic flow allocation of the road network, and the output of the upper-level planning model is used as the input of the lower-layer model. The lower-level planning model is a generalized minimum cost model with driving time, charging waiting time, charging time, and the cost of electricity consumed to reach the destination of the trip as objective functions. In this study, an empirical simulation is conducted on the road network of Hefei City, Anhui Province, utilizing three algorithms\u2014GA, GWO, and PSO\u2014for optimization and sensitivity analysis. The optimized results are compared with the existing charging station deployment scheme in the road network to demonstrate the effectiveness of the proposed methodology.<\/jats:p>","DOI":"10.3390\/a18080479","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T16:41:54Z","timestamp":1754325714000},"page":"479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Charging Station Siting and Capacity Determination Based on a Generalized Least-Cost Model of Traffic Distribution"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2986-5951","authenticated-orcid":false,"given":"Mingzhao","family":"Ma","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China"},{"name":"School of Automotive and Transportation Engineering, Hubei University of Arts and Science, Xiangyang 441053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Institute of Logistics Technology, Xiangyang 441100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lirong","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Automotive and Transportation Engineering, Hubei University of Arts and Science, Xiangyang 441053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4079-3445","authenticated-orcid":false,"given":"Yuhonghao","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China"},{"name":"School of Automotive and Transportation Engineering, Hubei University of Arts and Science, Xiangyang 441053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5705-9650","authenticated-orcid":false,"given":"Wenxin","family":"Li","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China"},{"name":"School of Automotive and Transportation Engineering, Hubei University of Arts and Science, Xiangyang 441053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.tranpol.2025.03.007","article-title":"How the synergy effect between renewable electricity deployment and terminal electrification mitigates transportation sectors\u2019 carbon emissions in China?","volume":"166","author":"Cui","year":"2025","journal-title":"Transp. Policy"},{"key":"ref_2","first-page":"104193","article-title":"Towards solar-energy-assisted electric vehicle charging stations: A literature review on site selection with GIS and MCDM methods","volume":"75","author":"Antila","year":"2025","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.egyr.2025.06.035","article-title":"Decision-analytics-based electric vehicle charging station location selection: A cutting-edge fuzzy rough framework","volume":"14","author":"Ecer","year":"2025","journal-title":"Energy Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"125242","DOI":"10.1016\/j.apenergy.2024.125242","article-title":"Electric bus charging station location selection problem with slow and fast charging","volume":"382","author":"Gkiotsalitis","year":"2025","journal-title":"Appl. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"110695","DOI":"10.1016\/j.cie.2024.110695","article-title":"Multi-objective model for electric vehicle charging station location selection problem for a sustainable transportation infrastructure","volume":"198","author":"Bilsel","year":"2024","journal-title":"Comput. Ind. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105182","DOI":"10.1016\/j.trc.2025.105182","article-title":"An exact optimization model for the electric bus charging station location problem under inter-station travel time and energy consumption uncertainties","volume":"178","author":"Dimitriadou","year":"2025","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110731","DOI":"10.1016\/j.engappai.2025.110731","article-title":"Application of multi-criteria decision-making method based on improved grade Z-number in site selection of new energy vehicles charging stations","volume":"151","author":"Fan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.trb.2012.09.007","article-title":"Optimal deployment of public charging stations for plug-in hybrid electric vehicles","volume":"47","author":"He","year":"2013","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ademulegun, O.O., MacArtain, P., Oni, B., and Hewitt, N.J. (2022). Multi-Stage Multi-Criteria Decision Analysis for Siting Electric Vehicle Charging Stations within and across Border Regions. Energies, 15.","DOI":"10.3390\/en15249396"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"129313","DOI":"10.1016\/j.jclepro.2021.129313","article-title":"A bi-level optimization model for electric vehicle charging strategy based on regional grid load following","volume":"325","author":"Yang","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.psep.2018.08.017","article-title":"Multi-objective site selection optimization of the gas-gathering station using NSGA-II","volume":"119","author":"Wang","year":"2018","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"S233","DOI":"10.1007\/s00521-018-3730-8","article-title":"Optimization of site selection for construction and demolition waste recycling plant using genetic algorithm","volume":"31","author":"Liu","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.trpro.2025.06.091","article-title":"Innovative Urban Planning: A Heuristic Approach Using the Simulated Annealing Algorithm to Optimize the Land Use Allocation for Enhancing Public Transportation Ridership","volume":"90","author":"Laddha","year":"2025","journal-title":"Transp. Res. Procedia"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1049\/rpg2.12916","article-title":"PSO-based optimal placement of electric vehicle charging stations in a distribution network in smart grid environment incorporating backward forward sweep method","volume":"18","author":"Altaf","year":"2024","journal-title":"IET Renew. Power Gener."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.ins.2017.07.038","article-title":"Globally-optimal prediction-based adaptive mutation particle swarm optimization","volume":"418\u2013419","author":"Cui","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e29579","DOI":"10.1016\/j.heliyon.2024.e29579","article-title":"Multi-loop active disturbance rejection control and PID control strategy for poultry house based on GA, PSO and GWO algorithms","volume":"10","author":"Elghardouf","year":"2024","journal-title":"Heliyon"},{"key":"ref_17","first-page":"292","article-title":"A hybrid PSO-GA algorithm for constrained optimization problems","volume":"274","author":"Garg","year":"2016","journal-title":"Appl. Math. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108672","DOI":"10.1016\/j.cie.2022.108672","article-title":"A parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportation","volume":"173","author":"Amirteimoori","year":"2022","journal-title":"Comput. Ind. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"115979","DOI":"10.1016\/j.apm.2025.115979","article-title":"Hybrid chaos game and grey wolf optimization algorithms for UAV path planning","volume":"142","author":"Yang","year":"2025","journal-title":"Appl. Math. Model."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wardrop, J.G. (1952). Some Theoretical Aspects of Road Traffic Research. Proceedings of the Institution of Civil Engineers, ICE Publishing.","DOI":"10.1680\/ipeds.1952.11259"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/8\/479\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:22:25Z","timestamp":1760034145000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/8\/479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,4]]},"references-count":20,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["a18080479"],"URL":"https:\/\/doi.org\/10.3390\/a18080479","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,8,4]]}}}