Autor según el artículo: Bustos, C; Rhoads, D; Sole-Ribalta, A; Masip, D; Arenas, A; Lapedriza, A; Borge-Holthoefer, J
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: Arenas Moreno, Alejandro / BORGE HOLTHOEFER, JAVIER
Palabras clave: Vehicles Urban transport Transportation safety Transportation planning Transportation development Traffic safety Sustainable transport systems Sustainability Public data source Public authorities Pedestrian safety Pedestrian Numerical model Mapillary Machine learning Internet Image segmentation Hazards Governmental initiatives Google street view Deep learning Data handling Convolutional neural networks Computer vision techniques Computational tools Computational methods Computational approach Artificial neural network Activation mapping Activation analysis
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.
Áreas temáticas: Transportation science & technology Transportation Management science and operations research Engenharias iv Engenharias iii Engenharias i Computer science applications Civil and structural engineering Ciencias sociales Ciência da computação Automotive engineering
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: alexandre.arenas@urv.cat
Identificador del autor: 0000-0003-0937-0334
Fecha de alta del registro: 2024-09-28
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S0968090X21000498?via%3Dihub
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Transportation Research Part C-Emerging Technologies. 125 (103018): 103018-
Referencia de l'ítem segons les normes APA: Bustos, C; Rhoads, D; Sole-Ribalta, A; Masip, D; Arenas, A; Lapedriza, A; Borge-Holthoefer, J (2021). Explainable, automated urban interventions to improve pedestrian and vehicle safety. Transportation Research Part C-Emerging Technologies, 125(103018), 103018-. DOI: 10.1016/j.trc.2021.103018
DOI del artículo: 10.1016/j.trc.2021.103018
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2021
Tipo de publicación: Journal Publications