Understanding multimodal mobility patterns of micromobility users in urban environments
Micromobility, which includes bicycle-sharing systems, e-scooters, and shared moped-style scooters, has emerged as a popular alternative to traditional transport modes in urban environments, thus expanding the number of transportation options available to urban travellers. Previous research has prim...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:301288 |
| Acceso en línea: | https://ddd.uab.cat/record/301288 https://dx.doi.org/urn:doi:10.1007/s11116-024-10531-3 |
| Access Level: | acceso abierto |
| Palabra clave: | Bicycle-sharing system E-scooter Micromobility Moped-style scooter sharing Multimodality Travel behaviour |
| Sumario: | Micromobility, which includes bicycle-sharing systems, e-scooters, and shared moped-style scooters, has emerged as a popular alternative to traditional transport modes in urban environments, thus expanding the number of transportation options available to urban travellers. Previous research has primarily relied on trip-based data to explore the multimodal character of micromobility. However, existing evidence has failed to understand the ways in which urban travellers have reshaped their mobility patterns as a consequence of the introduction of micromobility. Using a travel survey (N = 902) set in Barcelona, Spain, cluster techniques are used to group micromobility users according to their frequency of use of three different micromobility modes (bicycle-sharing systems, private e-scooter, and moped-style scooter-sharing services). Then, a multinomial logistic regression was used, in order to explore each cluster's usage of traditional modes of transport, along with all potential weekly combinations between modes. Results show that most micromobility users rely on a single type of micromobility mode on a weekly basis. The model further indicates that private e-scooter, shared bicycle, and shared moped-style scooter users develop different weekly mobility combination patterns. While personal micromobility options (private e-scooter) are associated with monomodal tendencies, sharing services (bicycle sharing and moped-style scooter sharing) encourage multimodal behaviours. These findings contribute to the limited knowledge concerning the role of some micromobility alternatives in creating more rational and less habit-dependent travel behaviour choices. |
|---|