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<p>The rapid growth in electric vehicle (EV) ownership has intensified the linkage between urban parking shortages and charging infrastructure constraints, rendering conventional parking-only layout frameworks inadequate. Therefore, this study proposes integrating EV charging demand into on-street parking layout optimization from a Transport-Energy Integration (TEI) perspective. First, a Stated Preference (SP) survey was conducted to analyze user charging choice behavior, and a Logit model was developed to quantify the effects of factors such as remaining battery level, walking distance, charging price, and parking discounts on charging probability. Second, a multi-objective on-street parking layout optimization model was constructed, aiming to maximize parking and charging demand satisfaction rates while minimizing parking facility construction costs and road segment saturation. Finally, a hybrid NSGA-II-AHA algorithm is developed to address the local convergence trap inherent in the standard NSGA-II algorithm. A case study conducted in Laicheng District, Jinan, demonstrates that the optimized layout fully satisfies parking demand (100%) and increases the charging demand satisfaction rate from 59% to 97%, highlighting the practicality and effectiveness of the proposed framework.</p>

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Optimization of on-street parking layout based on transport-energy integration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月24日

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  • Han Bai,
  • Yuxi Yuan,
  • Liang Zhao,
  • Tianzheng Wei,
  • Jian Lu,
  • Xiuguang Wang

摘要

The rapid growth in electric vehicle (EV) ownership has intensified the linkage between urban parking shortages and charging infrastructure constraints, rendering conventional parking-only layout frameworks inadequate. Therefore, this study proposes integrating EV charging demand into on-street parking layout optimization from a Transport-Energy Integration (TEI) perspective. First, a Stated Preference (SP) survey was conducted to analyze user charging choice behavior, and a Logit model was developed to quantify the effects of factors such as remaining battery level, walking distance, charging price, and parking discounts on charging probability. Second, a multi-objective on-street parking layout optimization model was constructed, aiming to maximize parking and charging demand satisfaction rates while minimizing parking facility construction costs and road segment saturation. Finally, a hybrid NSGA-II-AHA algorithm is developed to address the local convergence trap inherent in the standard NSGA-II algorithm. A case study conducted in Laicheng District, Jinan, demonstrates that the optimized layout fully satisfies parking demand (100%) and increases the charging demand satisfaction rate from 59% to 97%, highlighting the practicality and effectiveness of the proposed framework.