2026-04-06T12:25:22Zhttps://openaccess.uoc.edu/server/oai/request
oai:openaccess.uoc.edu:10609/1284672025-07-04T12:43:44Zcom_10609_67545com_10609_3741com_10609_67col_10609_67546col_10609_92386
Repositori Institucional (O2) author Bustos Rodriguez, Maria Cristina author Everett Rhoads, Daniel author Solé-Ribalta, Albert author Masip Rodó, David author Arenas, Alex author Lapedriza, Agata author Borge-Holthoefer, Javier other Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3) other Universitat Oberta de Catalunya (UOC) other MIT Media Lab 2021-02-19T11:12:14Z 2021-02-19T11:12:14Z 2020-10-23 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 0968-090X https://hdl.handle.net/10609/128467 10.1016/j.trc.2021.103018 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. eng CC BY-NC-ND deep learning Google Street View mapillary pedestrian traffic safety traffic safety aprendizaje automático Google Street View Mapillary peatonal seguridad vial aprenentatge automàtic Google Street View Mapillary peatonal seguretat del trànsit Explainable, automated urban interventions to improve pedestrian and vehicle safety info:eu-repo/semantics/article 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 URL https://openaccess.uoc.edu/bitstreams/ff1068d4-16a8-4e39-9351-50491d6deb2e/download File MD5 ee9ea895ece5b0bdf0ca4a12315cf745 22840116 application/pdf BRSRMALB_R2.pdf URL https://openaccess.uoc.edu/bitstreams/34d39ae1-c853-4bbe-a7a8-9d7c1ed373ee/download File MD5 5ef8198fa5c45ffa8b92a9530103dfcf 16560314 application/pdf BRSRMALB_si_R2.pdf