TY  - JOUR
AU  - Anh, Ly Hoang
AU  - Hussain, Altaf
PY  - 2026
DA  - 2026/04/22
TI  - An Integrated Demand Forecasting and Location Optimization Framework for Electric Vehicle Charging Stations: A Case Study of District 1, Ho Chi Minh City
JO  - ICCK Transactions on Advanced Computing and Systems
T2  - ICCK Transactions on Advanced Computing and Systems
JF  - ICCK Transactions on Advanced Computing and Systems
VL  - 2
IS  - 3
SP  - 173
EP  - 211
DO  - 10.62762/TACS.2026.319834
UR  - https://www.icck.org/article/abs/TACS.2026.319834
KW  - electric vehicles
KW  - intelligent charging infrastructure
KW  - K-Means
KW  - P-Median
KW  - advanced computing
KW  - location optimization
KW  - district 1 Ho Chi Minh City
KW  - EV charging demand forecasting
AB  - Vietnam's Electric Vehicle (EV) market is expanding rapidly, yet public charging infrastructure development lags significantly, exhibiting pronounced spatial imbalance in dense urban cores. This study addresses this gap through an integrated demand forecasting and location optimization framework for District 1, Ho Chi Minh City. We develop a log-linear regression model using Vietnam's macroeconomic data (2003–2023), identifying GDP and CPI as dominant determinants of vehicle ownership (R$^2$ = 0.962). Forecasted vehicle stocks for 2026–2030 are translated into public charging demand through vehicle-type disaggregation and service-capacity modeling. Spatially, we propose a four-stage optimization pipeline: demand point generation → K-Means spatial zoning → P-Median location optimization → budget-constrained allocation. Results reveal a multi-tier station distribution centered on commercial cores (Nguyen Hue–Ben Thanh corridor) with supplementary coverage in residential and tourist zones. Core areas require fast-charging expansion to manage 12\% annual demand growth, while northern/southern residential blocks need baseline coverage to eliminate service gaps. Critically, we demonstrate that profit-driven private deployment creates spatial inequity, necessitating government-coordinated planning to balance efficiency and accessibility. Despite data constraints, our framework provides a replicable methodology for charging infrastructure planning in high-density Southeast Asian urban contexts, with direct implications for Vietnam's green mobility transition.
SN  - 3068-7969
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Anh2026An,
  author = {Ly Hoang Anh and Altaf Hussain},
  title = {An Integrated Demand Forecasting and Location Optimization Framework for Electric Vehicle Charging Stations: A Case Study of District 1, Ho Chi Minh City},
  journal = {ICCK Transactions on Advanced Computing and Systems},
  year = {2026},
  volume = {2},
  number = {3},
  pages = {173-211},
  doi = {10.62762/TACS.2026.319834},
  url = {https://www.icck.org/article/abs/TACS.2026.319834},
  abstract = {Vietnam's Electric Vehicle (EV) market is expanding rapidly, yet public charging infrastructure development lags significantly, exhibiting pronounced spatial imbalance in dense urban cores. This study addresses this gap through an integrated demand forecasting and location optimization framework for District 1, Ho Chi Minh City. We develop a log-linear regression model using Vietnam's macroeconomic data (2003–2023), identifying GDP and CPI as dominant determinants of vehicle ownership (R\$^2\$ = 0.962). Forecasted vehicle stocks for 2026–2030 are translated into public charging demand through vehicle-type disaggregation and service-capacity modeling. Spatially, we propose a four-stage optimization pipeline: demand point generation → K-Means spatial zoning → P-Median location optimization → budget-constrained allocation. Results reveal a multi-tier station distribution centered on commercial cores (Nguyen Hue–Ben Thanh corridor) with supplementary coverage in residential and tourist zones. Core areas require fast-charging expansion to manage 12\\% annual demand growth, while northern/southern residential blocks need baseline coverage to eliminate service gaps. Critically, we demonstrate that profit-driven private deployment creates spatial inequity, necessitating government-coordinated planning to balance efficiency and accessibility. Despite data constraints, our framework provides a replicable methodology for charging infrastructure planning in high-density Southeast Asian urban contexts, with direct implications for Vietnam's green mobility transition.},
  keywords = {electric vehicles, intelligent charging infrastructure, K-Means, P-Median, advanced computing, location optimization, district 1 Ho Chi Minh City, EV charging demand forecasting},
  issn = {3068-7969},
  publisher = {Institute of Central Computation and Knowledge}
}