Applied Research Advances

Vol. 2 No. 1 (2026): Applied Research Advances
Articles

Examining Spatial Concordance of Knowledge Aggregated AHP TOPSIS and Random Forest for Electric Vehicle Charging Site Suitability

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  • Shahrier Main Mridul
Shahrier Main Mridul
Department of Civil and Environmental Engineering, Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Dhaka, Bangladesh

Published 2026-10-04

Keywords

  • Electric vehicle charging infrastructure,
  • AHP-TOPSIS,
  • Random Forest validation,
  • Expert elicitation

Copyright (c) 2026 Shahrier Main Mridul (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mridul, S. M. (2026). Examining Spatial Concordance of Knowledge Aggregated AHP TOPSIS and Random Forest for Electric Vehicle Charging Site Suitability. Applied Research Advances, 2(1), 337-359. https://doi.org/10.65069/ara21202636
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Abstract

Electric vehicle (EV) charging infrastructure planning in data-scarce megacities is often constrained by limited spatial data and expert-driven decision models whose underlying assumptions are rarely validated. This study presents an integrated GIS-based decision-support framework for identifying optimal EV charging locations in Dhaka, Bangladesh — a 22-million-resident megacity targeting 30% EV penetration by 2030, by combining expert-elicited Analytic Hierarchy Process (AHP) weighting with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Unlike conventional GIS-MCDM studies, the proposed framework systematically evaluates criterion collinearity and assesses the spatial concordance between expert-derived suitability patterns and a machine-learning model using Random Forest with spatial cross-validation. Fourteen spatial criteria derived from open-access datasets and weighted by eleven domain experts were used to generate a 100-m suitability map. The analysis identified fifteen priority charging zones, with the Tejgaon–Hatirjheel corridor ranking highest. Results showed strong agreement between expert- and machine-learning-derived suitability surfaces (Spearman's ρ = 0.968; top-zone overlap = 73.8%), while revealing substantial collinearity between transit and fuel station proximity, highlighting a potential source of bias in conventional MCDM applications. Built entirely on open data, the proposed framework offers a transparent, reproducible, and transferable methodology for EV charging infrastructure planning in data-constrained cities.

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References

  1. IEA. (2024). Global EV outlook 2024. International Energy Agency. https://www.iea.org/reports/global-ev-outlook-2024
  2. Gnann, T., Funke, S., Jakobsson, N., Plötz, P., Sprei, F., & Bennehag, A. (2018). Fast charging infrastructure for electric vehicles: Today's situation and future needs. Transportation Research Part D: Transport and Environment, 62, 314–329. https://doi.org/10.1016/j.trd.2018.03.004
  3. Tilly, N., Yigitcanlar, T., Degirmenci, K., & Paz, A. (2025). Systems-based approach to public electric vehicle supply equipment expansion: An international policy analysis. Sustainable Cities and Society, 131, Article 106739. https://doi.org/10.1016/j.scs.2025.106739
  4. Adnan, N., Nordin, S. M., Rahman, I., & Rasli, A. M. (2017). A new era of sustainable transport: An experimental examination on forecasting adoption behavior of EVs among Malaysian consumers. Transportation Research Part A: Policy and Practice, 103, 279–295. https://doi.org/10.1016/j.tra.2017.06.010
  5. Ministry of Industries. (2021). Automobile Industry Development Policy 2021. Government of the People's Republic of Bangladesh. https://www.dpp.gov.bd/upload_file/gazettes/41158_88374.pdf
  6. Bangladesh Power Development Board (BPDB). (2023). Annual report 2022–23. Ministry of Power, Energy and Mineral Resources, Government of Bangladesh.
  7. Bangladesh Bureau of Statistics (BBS). (2022). Population and Housing Census 2022: Preliminary report. Government of Bangladesh.
  8. Sani, G. M., Abas, A. M., Yusoff, N., & Said, M. F. (2023). Site selection for electric vehicle charging stations using GIS with MCDM AHP, FAHP and TOPSIS techniques: A review. IOP Conference Series: Earth and Environmental Science, 1274(1), Article 012019. https://doi.org/10.1088/1755-1315/1274/1/012019
  9. Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill.
  10. Forman, E., & Peniwati, K. (1998). Aggregating individual judgments and priorities with the analytic hierarchy process. European Journal of Operational Research, 108(1), 165–169. https://doi.org/10.1016/S0377-2217(97)00244-0
  11. Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9
  12. Ouerghi, S., Elsheikh, R., Amini, H., & Aldosari, S. (2026). An integrated GIS–AHP–sensitivity analysis framework for electric vehicle charging station site suitability in Qatar. ISPRS International Journal of Geo-Information, 15(2), Article 54. https://doi.org/10.3390/ijgi15020054
  13. Liu, C., Peng, Z., Liu, L., & Wu, H. (2024). Analysis of spatiotemporal characteristics and influencing factors of electric vehicle charging based on multisource data. ISPRS International Journal of Geo-Information, 13(2), Article 37. https://doi.org/10.3390/ijgi13020037
  14. Kumar, B., Kumar, M., Singh, P., & Singh, P. (2026). Multi-criteria decision analysis for locating electric vehicle charging stations using GIS & remote sensing. Case Studies on Transport Policy, 24, Article 101806. https://doi.org/10.1016/j.cstp.2026.101806
  15. Guo, S., & Zhao, H. (2015). Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective. Applied Energy, 158, 390–402. https://doi.org/10.1016/j.apenergy.2015.08.082
  16. Güler, D., & Yomralıoğlu, T. (2018). Optimal location selection for electric vehicle charging stations using GIS. In Proceedings of the 7th International Conference on Cartography and GIS (pp. 340–345). https://web.itu.edu.tr/gulerdo/papers/3.pdf
  17. Kaya, Ö., Tortum, A., Alemdar, K. D., & Çodur, M. Y. (2020). Site selection for EVCS in Istanbul by GIS and multi-criteria decision-making. Transportation Research Part D: Transport and Environment, 80, Article 102271. https://doi.org/10.1016/j.trd.2020.102271
  18. Zhang, Y., Teoh, B. K., & Zhang, L. (2022). Integrated Bayesian networks with GIS for electric vehicles charging site selection. Journal of Cleaner Production, 344, Article 131049. https://doi.org/10.1016/j.jclepro.2022.131049
  19. Jia, L., Hu, Z., Song, Y., & Luo, Z. (2012). Optimal siting and sizing of electric vehicle charging stations. In 2012 IEEE International Electric Vehicle Conference (pp. 1–6). https://doi.org/10.1109/IEVC.2012.6183283
  20. He, S., Kuo, Y.-H., & Wu, D. (2016). Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: A case study of Beijing, China. Transportation Research Part C: Emerging Technologies, 67, 131–148. https://doi.org/10.1016/j.trc.2016.02.003
  21. Mhana, K. H., & Awad, H. A. (2024). An ideal location selection of electric vehicle charging stations: Employment of integrated analytical hierarchy process with geographical information system. Sustainable Cities and Society, 107, Article 105456. https://doi.org/10.1016/j.scs.2024.105456
  22. Rashmitha, Y., Sushma, M. B., & Roy, S. (2024). A novel multi-criteria framework for selecting optimal sites for electric vehicle charging stations from a sustainable perspective: Evidence from India. Environment, Development and Sustainability, 28(8), 20237–20263. https://doi.org/10.1007/s10668-024-05746-4
  23. Guler, D., & Yomralioglu, T. (2020). Suitable location selection for the electric vehicle fast charging station with AHP and fuzzy AHP methods using GIS. Annals of GIS, 26(2), 169–189. https://doi.org/10.1080/19475683.2020.1737226
  24. Rao, R. V. (2013). Decision making in manufacturing environment using graph theory and fuzzy multiple attribute decision making methods. Springer. https://doi.org/10.1007/978-1-4471-4375-8
  25. Kordi, M., & Brandt, S. A. (2012). Effects of increasing fuzziness on analytic hierarchy process for spatial multicriteria decision analysis. Computers, Environment and Urban Systems, 36(1), 43–53. https://doi.org/10.1016/j.compenvurbsys.2011.07.004
  26. Erbas, M., Kabak, M., Özceylan, E., & Çetinkaya, C. (2018). Optimal siting of electric vehicle charging stations: A GIS-based fuzzy multi-criteria decision analysis. Energy, 163, 1017–1031. https://doi.org/10.1016/j.energy.2018.08.140
  27. Costa, E., Paiva, A., Seixas, J., Costa, G., Baptista, P., & Gallachóir, B. Ó. (2018). Spatial planning of electric vehicle infrastructure for Belo Horizonte, Brazil. Journal of Advanced Transportation, 2018, 1–16. https://doi.org/10.1155/2018/8923245
  28. Namdeo, A., Tiwary, A., & Dziurla, R. (2014). Spatial planning of public charging points using multi-dimensional analysis of early adopters of electric vehicles for a city region. Technological Forecasting and Social Change, 89, 188–200. https://doi.org/10.1016/j.techfore.2013.08.032
  29. OpenStreetMap contributors. (2024). OpenStreetMap planet dump. https://planet.openstreetmap.org
  30. WorldPop & Center for International Earth Science Information Network (CIESIN), Columbia University. (2018). Global high resolution population denominators project—Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://doi.org/10.5258/SOTON/WP00660
  31. Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. https://doi.org/10.1016/0377-2217(90)90057-I
  32. O'Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6
  33. Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., & Dormann, C. F. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913–929. https://doi.org/10.1111/ecog.02881
  34. Meyer, H., Reudenbach, C., Wöllauer, S., & Nauss, T. (2019). Importance of spatial predictor variable selection in machine learning applications. Ecological Modelling, 411, Article 108815. https://doi.org/10.1016/j.ecolmodel.2019.108815
  35. Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8(1), Article 25. https://doi.org/10.1186/1471-2105-8-25