Exploring key spatial determinants for mobility hub placement based on micromobility ridership
Over the past decade, cities have witnessed a surge in micromobility services that offer flexible mobility options for citizens on an as-needed basis, such as for covering the first/last mile connection of their trips. Although these services have known benefits, including reduced CO2 emissions and...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/107131 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/107131 |
| Access Level: | acceso abierto |
| Palabra clave: | 911.3:656 Shared mobility Mobility hubs Allocating models Micromobility usage Geografía 2505 Geografía |
| Sumario: | Over the past decade, cities have witnessed a surge in micromobility services that offer flexible mobility options for citizens on an as-needed basis, such as for covering the first/last mile connection of their trips. Although these services have known benefits, including reduced CO2 emissions and less public space required for parking, there is still insufficient understanding of their common dynamics and usage, which can support decision-making in the quest for allocating new mobility infrastructure, like mobility hubs. In this paper, we propose a methodology to identify potential mobility hub locations based on the common associated spatial factors with the ridership of different micromobility services (station-based bike-sharing, dockless moped-style scooter-sharing and scooter-sharing services) in Madrid, Spain. We identify the common associated spatial factors with micromobility usage (e.g. bike stations' density, commercial land use and cycling infrastructure) and train linear models to explore which dependent variables represents better a “common ridership” of multiple micromobility services while fitting better that data. Subsequently, we test our models in a different area to identify potential hotspots for suggested locations. Findings show that considering micromobility ridership altogether using principal component analysis provides better ridership estimations in the test areas. The methodology has the potential to be replicable in other cities and guide decision-making processes for searching potential mobility hub locations. |
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