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http://hdl.handle.net/10609/128467
Título : | Explainable, automated urban interventions to improve pedestrian and vehicle safety |
Autoría: | Bustos Rodriguez, Maria Cristina Everett Rhoads, Daniel Solé-Ribalta, Albert Masip Rodó, David Arenas, Alex Lapedriza, Agata Borge-Holthoefer, Javier |
Otros: | Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3) Universitat Oberta de Catalunya (UOC) MIT Media Lab |
Citación : | C. Bustos, D. Rhoads, A. Solé-Ribalta, D. Masip, A. Arenas, A. Lapedriza, J. Borge-Holthoefer. Explainable, automated urban interventions to improve pedestrian and vehicle safety, Transportation Research Part C: Emerging Technologies, Volume 125, 2021. doi: https://doi.org/10.1016/j.trc.2021.103018 |
Resumen : | At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e.g. the problems of congestion and pollution. And yet, we cannot disregard the most vulnerable elements in the urban landscape: pedestrians, exposed to higher risks than other road users. Indeed, safe, accessible, and sustainable transport systems in cities are a core target of the UN's 2030 Agenda. Thus, there is an opportunity to apply advanced computational tools to the problem of traffic safety, in regards especially to pedestrians, who have been often overlooked in the past. This paper combines public data sources, large-scale street imagery and computer vision techniques to approach pedestrian and vehicle safety with an automated, relatively simple, and universally-applicable data-processing scheme. The steps involved in this pipeline include the adaptation and training of a Residual Convolutional Neural Network to determine a hazard index for each given urban scene, as well as an interpretability analysis based on image segmentation and class activation mapping on those same images. Combined, the outcome of this computational approach is a fine-grained map of hazard levels across a city, and an heuristic to identify interventions that might simultaneously improve pedestrian and vehicle safety. The proposed framework should be taken as a complement to the work of urban planners and public authorities. |
Palabras clave : | aprendizaje automático Google Street View Mapillary peatonal seguridad vial |
DOI: | 10.1016/j.trc.2021.103018 |
Tipo de documento: | info:eu-repo/semantics/article |
Versión del documento: | info:eu-repo/semantics/submittedVersion |
Fecha de publicación : | 23-oct-2020 |
Licencia de publicación: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Aparece en las colecciones: | Articles Articles cientÍfics |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
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BRSRMALB_R2.pdf | article | 22,3 MB | Adobe PDF | Visualizar/Abrir |
BRSRMALB_si_R2.pdf | data source | 16,17 MB | Adobe PDF | Visualizar/Abrir |
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