Transport behavior-mining from smartphones: a review
Abstract Background Although people and smartphones have become almost inseparable, especially during travel, smartphones still represent a small fraction of a complex multi-sensor platform enabling the passive collection of users’ travel behavior. Smartphone-based travel survey data yields the rich...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
SpringerOpen
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/16bbcfee4dab4f1285bfc92e5f514a01 |
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| Sumario: | Abstract Background Although people and smartphones have become almost inseparable, especially during travel, smartphones still represent a small fraction of a complex multi-sensor platform enabling the passive collection of users’ travel behavior. Smartphone-based travel survey data yields the richest perspective on the study of inter- and intrauser behavioral variations. Yet after over a decade of research and field experimentation on such surveys, and despite a consensus in transportation research as to their potential, smartphone-based travel surveys are seldom used on a large scale. Purpose This literature review pinpoints and examines the problems limiting prior research, and exposes drivers to select and rank machine-learning algorithms used for data processing in smartphone-based surveys. Conclusion Our findings show the main physical limitations from a device perspective; the methodological framework deployed for the automatic generation of travel-diaries, from the application perspective; and the relationship among user interaction, methods, and data, from the ground truth perspective. |
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