In recent years, the micromobility and the usage of shared electric kick scooters (e-kscooters) have been constantly growing, especially for systematic and recreational trips in large urban areas. Micromobility might be seen as a well-suited last-mile solution by providing a flexible travel service connection with public transport and MaaS (Mobility as a Service), in general. However, there is a need for implementing adequate regulations regarding safety aspects and shared e-kscooter parking locations, but also for meeting the user requirements. The choice of optimal shared e-kscooter parking locations could help decision-makers to regulate unmanaged dock-less shared e-kscooter parking spots that could generate issues for other road users. To this end, in this paper, a novel multi-objective Micromobility Maximal Coverage Parking Location model (M-MCPL) is developed. The model has been solved by applying an elitist Genetic Algorithm that returns the optimal shared e-kscooter parking locations based on the following objective functions: i) the maximization of the population coverage; ii) the maximization of multimodal accessibility coverage (i.e., bus, railway, and metro modes); iii) the maximization of the attraction coverage considering the most relevant points of interest for each corresponding zone in large urban areas. The proposed M-MCPL model has been applied to the case of Rome (Italy) and results suggest priorities for the shared e-kscooter parking locations design. Furthermore, the proposed model is flexible and can be considered as a decision support tool for decision-makers when planning dedicated services in different large urban areas. For that purpose, we conducted the sensitivity analysis by focusing on the single-objective model in which decision-makers might be interested in providing only high accessibility to transport services or maximizing potential demand.

中文翻译：

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大城市地区共享电动滑板车停车位设计的多目标模型

近年来，微出行和共享电动滑板车（e-kscooters）的使用不断增长，特别是在大城市地区的系统性和休闲性出行。一般来说，微移动可能被视为一种非常适合的最后一英里解决方案，它提供了与公共交通和 MaaS（移动即服务）的灵活旅行服务连接。然而，需要在安全方面和共享电动滑板车停车位置方面实施适当的法规，同时也需要满足用户的要求。选择最佳的共享电动滑板车停车位置可以帮助决策者监管不受管理的无码头共享电动滑板车停车位，这些停车位可能会给其他道路使用者带来问题。为此，本文开发了一种新颖的多目标微移动最大覆盖停车位置模型（M-MCPL）。该模型通过应用精英遗传算法来解决，该算法根据以下目标函数返回最佳共享电动滑板车停车位置：i）人口覆盖范围的最大化； ii) 多式联运无障碍覆盖范围最大化（即公共汽车、铁路和地铁模式）； iii) 考虑大城市地区每个相应区域最相关的兴趣点，最大化景点覆盖范围。拟议的 M-MCPL 模型已应用于罗马（意大利）的案例，结果表明共享电动滑板车停车位设计的优先顺序。此外，所提出的模型非常灵活，可以被视为决策者在规划不同大城市地区的专用服务时的决策支持工具。为此，我们通过关注单目标模型进行了敏感性分析，在该模型中，决策者可能只对提供交通服务的高可达性或最大化潜在需求感兴趣。