4.5 Article

Parking Places to Moped-Style Scooter Sharing Services Using GIS Location-Allocation Models and GPS Data

Journal

Publisher

MDPI
DOI: 10.3390/ijgi10040230

Keywords

shared mobility; moped-style scooter sharing; parking places; GIS location-allocation models; spatio-temporal analysis

Funding

  1. European Regional Development Fund
  2. Comunidad de Madrid [H2019/HUM-5761]

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This paper proposes a methodology for finding parking spaces for shared motorcycle services using GIS and GPS data, with a case study in the center of Madrid. The results show that reserving a certain number of parking spaces can capture the majority of travel demand, but the variation in demand distribution affects the efficiency of the network.
Moped-style scooters are one of the most popular systems of micro-mobility. They are undoubtedly good for the city, as they promote forms of environmentally-friendly mobility, in which flexibility helps prevent traffic build-up in the urban centers where they operate. However, their increasing numbers are also generating conflicts as a result of the bad behavior of users, their unwarranted use in public spaces, and above all their parking. This paper proposes a methodology for finding parking spaces for shared motorcycle services using Geographic information system (GIS) location-allocation models and Global Positioning System (GPS) data. We used the center of Madrid and data from the company Muving (one of the city's main operators) for our case study. As well as finding the location of parking spaces for motorbikes, our analysis examines how the varying distribution of demand over the course of the day affects the demand allocated to parking spaces. The results demonstrate how reserving a relatively small number of parking spaces for scooters makes it possible to capture over 70% of journeys in the catchment area. The daily variations in the distribution of demand slightly reduce the efficiency of the network of parking spaces in the morning and increase it at night, when demand is strongly focused on the most central areas.

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