A multi-objective model to design shared e-kick scooters parking spaces in large urban areas

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.