Li Xiaodan, Guo Yunci, Liu Zhen, Sun Dandan, Liu Yidi, Wang Wencan
State Key Laboratory for Tunnel Engineering, Beijing, China.
China University of Mining and Technology, Beijing, China.
PLoS One. 2025 Jun 20;20(6):e0326455. doi: 10.1371/journal.pone.0326455. eCollection 2025.
The acceleration of global urbanization and the rapid growth of urban populations have intensified the complexity and urgency of parking demand. In megacities with limited land resources, efficiently addressing diverse parking needs has become a critical issue for sustainable urban development. Multi-objective optimization methods are widely applied to tackle such challenges, providing decision-makers with a set of optimal solutions that balance multiple objectives. However, existing studies often lack quantitative analyses of the relationships among these solutions, limiting their applicability in accommodating decision-makers with varying preferences. This study focuses on Jing'an District in Shanghai, a representative region of a Chinese megacity, to address this global issue. Based on real-world data, a multi-objective optimization model is constructed considering convenience, coverage, and cost-efficiency. The model is solved using an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), which dynamically adjusts crossover and mutation rates. Furthermore, the Pareto solution set is quantitatively analyzed from a cost-benefit perspective by integrating marginal benefit theory. This approach provides robust support for decision-makers seeking an optimal balance between cost and benefit, offering scenario-specific strategies. The findings of this study not only present an innovative, systematic, and flexible solution to the "parking dilemma" in high-density residential areas but also provide practical guidance and insights for other large cities in the planning and implementation of smart underground parking facilities.
全球城市化的加速和城市人口的快速增长加剧了停车需求的复杂性和紧迫性。在土地资源有限的特大城市中，有效满足多样化的停车需求已成为城市可持续发展的关键问题。多目标优化方法被广泛应用于应对此类挑战，为决策者提供一系列平衡多个目标的最优解决方案。然而，现有研究往往缺乏对这些解决方案之间关系的定量分析，限制了它们在满足不同偏好决策者方面的适用性。本研究以上海静安区为重点，这是中国特大城市的一个代表性区域，以解决这一全球性问题。基于实际数据，构建了一个考虑便利性、覆盖率和成本效益的多目标优化模型。该模型使用改进的非支配排序遗传算法II（NSGA-II）进行求解，该算法可动态调整交叉率和变异率。此外，通过整合边际效益理论，从成本效益角度对帕累托解集进行定量分析。这种方法为寻求成本与效益最佳平衡的决策者提供了有力支持，并提供了针对具体场景的策略。本研究的结果不仅为高密度住宅区的“停车困境”提供了创新、系统和灵活的解决方案，也为其他大城市在智能地下停车设施的规划和实施方面提供了实际指导和见解。
