{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:32:31Z","timestamp":1775561551478,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Gansu Province Science Foundation for Youths","award":["24JRRA273"],"award-info":[{"award-number":["24JRRA273"]}]},{"name":"Gansu Province Science Foundation for Youths","award":["2024A-065"],"award-info":[{"award-number":["2024A-065"]}]},{"name":"Gansu Province Education Science and Technology Innovation Project","award":["24JRRA273"],"award-info":[{"award-number":["24JRRA273"]}]},{"name":"Gansu Province Education Science and Technology Innovation Project","award":["2024A-065"],"award-info":[{"award-number":["2024A-065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>With the rapid expansion of the electric vehicle (EV) market, optimizing the distribution of charging stations has attracted increasing attention. Unlike internal combustion engine vehicles, EVs are typically charged at the end of a trip rather than during transit. Therefore, analyzing EV users\u2019 charging preferences based on their activity\u2013travel patterns is essential. This study seeks to improve the operational efficiency and accessibility of EV charging stations in Lanzhou City by optimizing their spatial distribution. To achieve this, a novel multi-objective optimization model integrating NSGA-III and TOPSIS is proposed. The methodology consists of two key steps. First, the NSGA-III algorithm is applied to optimize three objective functions: minimizing construction costs, maximizing user satisfaction, and maximizing user convenience, thereby identifying charging station locations that address diverse needs. Second, the TOPSIS method is employed to rank and evaluate various location solutions, ultimately determining the final sitting strategy. The results show that the 232 locations obtained by the optimization model are reasonably distributed, with good operational efficiency and convenience. Most of them are distributed in urban centers and commercial areas, which is consistent with the usage scenarios of EV users. In addition, this study demonstrates the superiority in determining the distribution of charging station locations of the proposed method. In summary, this study determined the optimal distribution of 232 EV charging stations in Lanzhou City using multi-objective optimization and ranking methods. The results are of great significance for improving the operational efficiency and convenience of charging station location optimization and offer valuable insights for other cities in northwestern China in planning their charging infrastructure.<\/jats:p>","DOI":"10.3390\/ijgi14100373","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T07:46:49Z","timestamp":1758786409000},"page":"373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Optimization of Electric Vehicle Charging Station Location Distribution Based on Activity\u2013Travel Patterns"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0937-0812","authenticated-orcid":false,"given":"Qian","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Gansu Provincial Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou 730070, China"}]},{"given":"Guiwu","family":"Si","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"}]},{"given":"Hongyi","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1080\/15568318.2024.2449436","article-title":"Impacts of electric vehicles on traffic-power systems: A review","volume":"19","author":"Wang","year":"2025","journal-title":"Int. J. Sustain. Transp."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1002\/pam.22362","article-title":"The role of government in the market for electric vehicles: Evidence from China","volume":"41","author":"Li","year":"2022","journal-title":"J. Policy Anal. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101771","DOI":"10.1016\/j.techsoc.2021.101771","article-title":"Electric vehicle industry sustainable development with a stakeholder engagement system","volume":"67","author":"Cao","year":"2021","journal-title":"Technol. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1287\/trsc.2022.0253","article-title":"Electric vehicle scheduling in public transit with capacitated charging stations","volume":"58","author":"Dollevoet","year":"2024","journal-title":"Transp. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1080\/15568318.2025.2471773","article-title":"Qualitative insights into travel behavior change from using private cars to shared cars","volume":"19","author":"Hou","year":"2025","journal-title":"Int. J. Sustain. Transp."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Thorhauge, M., Rich, J., and Mabit, S.E. (2024). Charging behaviour and range anxiety in long-distance EV travel: An adaptive choice design study. Transportation.","DOI":"10.1007\/s11116-024-10561-x"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jtrangeo.2016.02.002","article-title":"Charging station location problem of plug-in electric vehicles","volume":"52","author":"Zhu","year":"2016","journal-title":"J. Transp. Geogr."},{"key":"ref_8","first-page":"75","article-title":"Optimizing the deployment of electric vehicle charging stations using pervasive mobility data","volume":"121","author":"Vazifeh","year":"2019","journal-title":"Transp. Res. Part A: Policy Pract."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.eswa.2015.11.007","article-title":"Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS","volume":"50","author":"Tavana","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, S., Shi, Y., Chen, X., and Qi, F. (2015, January 19\u201321). Optimal location of electric vehicle charging stations using genetic algorithm. Proceedings of the 2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS), Busan, Republic of Korea.","DOI":"10.1109\/APNOMS.2015.7275344"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.trc.2016.01.008","article-title":"A multi-period optimization model for the deployment of public electric vehicle charging stations on network","volume":"65","author":"Li","year":"2016","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","article-title":"An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints","volume":"18","author":"Deb","year":"2013","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4749","DOI":"10.1109\/TITS.2019.2946209","article-title":"A multi-objective emergency rescue facilities location model for catastrophic interlocking chemical accidents in chemical parks","volume":"21","author":"Men","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","first-page":"10","article-title":"Optimal placement of an unified power flow controller in a transmission network by unified non dominated sorting genetic algorithm-III and differential evolution algorithm","volume":"5","author":"Arouna","year":"2019","journal-title":"Int. J. Electr. Compon. Energy Convers."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.ins.2018.06.055","article-title":"Modified NSGA-III for sensor placement in water distribution system","volume":"509","author":"Hu","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1080\/15568318.2025.2490488","article-title":"Costs and carbon: Evaluating the tradeoffs in Taiwan\u2019s shift toward electric vehicles","volume":"19","author":"Tseng","year":"2025","journal-title":"Int. J. Sustain. Transp."},{"key":"ref_17","first-page":"1","article-title":"Electric Vehicle Charging Station Location by Applying Optimization Approach","volume":"6","author":"Shoushtari","year":"2024","journal-title":"Int. J. Ind. Eng. Oper. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11504","DOI":"10.1016\/j.egyr.2022.09.011","article-title":"An in-depth analysis of electric vehicle charging station infrastructure, policy implications, and future trends","volume":"8","author":"Mastoi","year":"2022","journal-title":"Energy Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"130701","DOI":"10.1016\/j.energy.2024.130701","article-title":"A Type-2 fuzzy hybrid preference optimization methodology for electric vehicle charging station location","volume":"293","author":"Men","year":"2024","journal-title":"Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1080\/15568318.2025.2476102","article-title":"Study on consumers\u2019 electric vehicle choice preferences in severe cold regions: Based on choice experiments in Heilongjiang province","volume":"19","author":"Chen","year":"2025","journal-title":"Int. J. Sustain. Transp."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1287\/trsc.2023.0247","article-title":"Modeling and solving the traveling salesman problem with speed optimization for a plug-in hybrid electric vehicle","volume":"58","author":"Wu","year":"2024","journal-title":"Transp. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100422","DOI":"10.1016\/j.segan.2020.100422","article-title":"Allocation of electric vehicle charging station considering uncertainties","volume":"25","author":"Pal","year":"2021","journal-title":"Sustain. Energy Grids Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108868","DOI":"10.1016\/j.epsr.2022.108868","article-title":"Application of AOA algorithm for optimal placement of electric vehicle charging station to minimize line losses","volume":"214","author":"Kathiravan","year":"2023","journal-title":"Electr. Power Syst. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"123975","DOI":"10.1016\/j.apenergy.2024.123975","article-title":"Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation","volume":"375","author":"Liu","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.ijhydene.2024.09.029","article-title":"A particle swarm optimizer-based optimization approach for locating electric vehicles charging stations in smart cities","volume":"87","author":"Aljaidi","year":"2024","journal-title":"Int. J. Hydrog. Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101122","DOI":"10.1016\/j.esr.2023.101122","article-title":"Spatial adaptability evaluation and optimal location of electric vehicle charging stations: A win-win view from urban travel dynamics","volume":"49","author":"Wu","year":"2023","journal-title":"Energy Strategy Rev."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103264","DOI":"10.1016\/j.trd.2022.103264","article-title":"Electric vehicle demand estimation and charging station allocation using urban informatics","volume":"106","author":"Yi","year":"2022","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"136022","DOI":"10.1016\/j.jclepro.2023.136022","article-title":"Random parameters modeling of charging-power demand for the optimal location of electric vehicle charge facilities","volume":"388","author":"Hamed","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1754","DOI":"10.1109\/TITS.2024.3498917","article-title":"Multi-Objective Planning Optimization of Electric Vehicle Charging Stations With Coordinated Spatiotemporal Charging Demand","volume":"26","author":"Zhu","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"131049","DOI":"10.1016\/j.jclepro.2022.131049","article-title":"Integrated Bayesian networks with GIS for electric vehicles charging site selection","volume":"344","author":"Zhang","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1016\/j.ijhydene.2024.07.067","article-title":"Multi-period urban hydrogen refueling stations site selection and capacity planning with many-objective optimization for hydrogen supply chain","volume":"79","author":"Zhou","year":"2024","journal-title":"Int. J. Hydrog. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"137681","DOI":"10.1016\/j.jclepro.2023.137681","article-title":"Joint optimization of infrastructure deployment and fleet operations for an electric carsharing system by considering multi-type vehicles","volume":"414","author":"Sai","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102813","DOI":"10.1016\/j.trd.2021.102813","article-title":"Electric vehicle charging network in Europe: An accessibility and deployment trends analysis","volume":"94","author":"Falchetta","year":"2021","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8640","DOI":"10.1109\/TTE.2024.3362707","article-title":"Online Multi-Objective Optimization for Electric Vehicle Charging Station Operation","volume":"10","author":"Li","year":"2024","journal-title":"IEEE Trans. Transp. Electrif."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"126153","DOI":"10.1016\/j.jclepro.2021.126153","article-title":"Multiobjective optimization of building energy consumption based on BIM-DB and LSSVM-NSGA-II","volume":"294","author":"Chen","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"135312","DOI":"10.1016\/j.jclepro.2022.135312","article-title":"Configuration optimization of an off-grid multi-energy microgrid based on modified NSGA-II and order relation-TODIM considering uncertainties of renewable energy and load","volume":"383","author":"Lu","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"140306","DOI":"10.1016\/j.jclepro.2023.140306","article-title":"Multi-objective land use optimization based on integrated NSGA\u2013II\u2013PLUS model: Comprehensive consideration of economic development and ecosystem services value enhancement","volume":"434","author":"Luan","year":"2024","journal-title":"J. Clean. Prod."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"142746","DOI":"10.1016\/j.jclepro.2024.142746","article-title":"Enhancing mix proportion design of low carbon concrete for shield segment using a combination of Bayesian optimization-NGBoost and NSGA-III algorithm","volume":"465","author":"Cao","year":"2024","journal-title":"J. Clean. Prod."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"116739","DOI":"10.1016\/j.eswa.2022.116739","article-title":"Electric vehicle charging stations emplacement using genetic algorithms and agent-based simulation","volume":"197","author":"Palanca","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"123437","DOI":"10.1016\/j.energy.2022.123437","article-title":"Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm","volume":"247","author":"Zhou","year":"2022","journal-title":"Energy"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4920","DOI":"10.1109\/TTE.2024.3472459","article-title":"Location Optimization of Charging Stations for Electric Vehicles Based on Heterogeneous Factors Analysis and Improved Genetic Algorithm","volume":"11","author":"Shuai","year":"2025","journal-title":"IEEE Trans. Transp. Electrif."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1111\/mice.12853","article-title":"Optimal planning of flood-resilient electric vehicle charging stations","volume":"38","author":"Zhang","year":"2023","journal-title":"Comput. -Aided Civ. Infrastruct. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"104180","DOI":"10.1016\/j.est.2022.104180","article-title":"A comprehensive planning framework for electric vehicles fast charging station assisted by solar and battery based on Queueing theory and non-dominated sorting genetic algorithm-II in a co-ordinated transportation and power network","volume":"49","author":"Kumar","year":"2022","journal-title":"J. Energy Storage"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"116945","DOI":"10.1016\/j.energy.2020.116945","article-title":"Optimal battery electric vehicles range: A study considering heterogeneous travel patterns, charging behaviors, and access to charging infrastructure","volume":"197","author":"Zhou","year":"2020","journal-title":"Energy"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Hu, Y., Tan, W., Li, C., and Ding, Z. (2020). Dynamic time-of-use pricing strategy for electric vehicle charging considering user satisfaction degree. Appl. Sci., 10.","DOI":"10.3390\/app10093247"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.enpol.2011.12.041","article-title":"The economics of fast charging infrastructure for electric vehicles","volume":"43","author":"Schroeder","year":"2012","journal-title":"Energy Policy"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.enpol.2015.12.007","article-title":"Methodology for assessing electric vehicle charging infrastructure business models","volume":"89","author":"Madina","year":"2016","journal-title":"Energy Policy"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.trd.2015.04.015","article-title":"What is the market potential of plug-in electric vehicles as commercial passenger cars? A case study from Germany","volume":"37","author":"Gnann","year":"2015","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.enpol.2012.09.059","article-title":"Cost-effectiveness of plug-in hybrid electric vehicle battery capacity and charging infrastructure investment for reducing US gasoline consumption","volume":"52","author":"Peterson","year":"2013","journal-title":"Energy Policy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/s41560-018-0136-x","article-title":"Planning for electric vehicle needs by coupling charging profiles with urban mobility","volume":"3","author":"Xu","year":"2018","journal-title":"Nat. Energy"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hwang, C.L., and Yoon, K. (1981). Methods for multiple attribute decision making. Multiple Attribute Decision Making: Methods and Applications a State-of-the-Art Survey, Springer.","DOI":"10.1007\/978-3-642-48318-9_3"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.omega.2018.07.004","article-title":"Generalised framework for multi-criteria method selection","volume":"86","author":"Jankowski","year":"2019","journal-title":"Omega"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/10\/373\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:49:21Z","timestamp":1760035761000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/10\/373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,25]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["ijgi14100373"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14100373","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,25]]}}}