Using Hierarchical Facility Location, Single Facility Approach, and GIS in Carsharing Services
In the last few years, vehicle sharing has driven a gradual switch from ownership-based private mobility to service usage as a sustainable urban transport alternative. A significant number of cities have implemented mobility sharing programs. Shared transport reduces both traffic congestion, and the...
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/49cff19db31442d49cd8887824c9a29a |
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| Sumario: | In the last few years, vehicle sharing has driven a gradual switch from ownership-based private mobility to service usage as a sustainable urban transport alternative. A significant number of cities have implemented mobility sharing programs. Shared transport reduces both traffic congestion, and the need for parking space, decreasing the number of vehicles on the road. The optimization of shared mobility service sites increases potential user access, reduces transportation costs, and augments demand for this transportation modality. Car sharing is a mobility concept where the usage of a vehicle fleet is shared among several people. This is a relatively new concept of transport, with short vehicle rental periods. It provides the convenience of private vehicles without additional charges. A key success factor is the location of sharing stations. The study presented here refers to a car sharing service to be operated by a carmaker in the city of São Paulo (Brazil). This article aims to identify and to select the best places to establish sharing stations within the company’s dealer and servicing network. A geographic information system (GIS) calculates spatial distribution of potential trip demand. Two models of hierarchical facility location are used to determine ideal station locations. It also suggests potential local partners to house car-sharing stations, such as hotels and private car parks. Voronoi diagrams support the location task. The recent rediscovery of Weber’s classic unique facility location problem has also been applied. The selection criterion was to maximize demand and hence operator profit, while minimizing obstacles like the distance to stations. |
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