Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/112647
DC FieldValueLanguage
dc.contributor.authorRita, Luís-
dc.contributor.authorPeliteiro, Miguel-
dc.contributor.authorBostan, Tudor-Codrin-
dc.contributor.authorTamagusko, Tiago Barreto-
dc.contributor.authorFerreira, Adelino-
dc.date.accessioned2024-02-02T11:41:40Z-
dc.date.available2024-02-02T11:41:40Z-
dc.date.issued2023-
dc.identifier.issn2071-1050-
dc.identifier.urihttps://hdl.handle.net/10316/112647-
dc.description.abstractCycling is a sustainable mode of transportation with significant benefits for society. The number of cyclists on the streets depends heavily on their perception of safety, which makes it essential to establish a common metric for determining and comparing risk factors related to road safety. This research addresses the identification of cyclists’ risk factors using deep learning techniques applied to a Google Street View (GSV) imagery dataset. The research utilizes a case study approach, focusing on London, and applies object detection and image segmentation models to extract cyclists’ risk factors from GSV images. Two state-of-the-art tools, You Only Look Once version 5 (YOLOv5) and the pyramid scene parsing network (PSPNet101), were used for object detection and image segmentation. This study analyzes the results and discusses the technology’s limitations and potential for improvements in assessing cyclist safety. Approximately 2 million objects were identified, and 250 billion pixels were labeled in the 500,000 images available in the dataset. On average, 108 images were analyzed per Lower Layer Super Output Area (LSOA) in London. The distribution of risk factors, including high vehicle speed, tram/train rails, truck circulation, parked cars and the presence of pedestrians, was identified at the LSOA level using YOLOv5. Statistically significant negative correlations were found between cars and buses, cars and cyclists, and cars and people. In contrast, positive correlations were observed between people and buses and between people and bicycles. Using PSPNet101, building (19%), sky (15%) and road (15%) pixels were the most common. The findings of this research have the potential to contribute to a better understanding of risk factors for cyclists in urban environments and provide insights for creating safer cities for cyclists by applying deep learning techniques.pt
dc.language.isoengpt
dc.publisherMDPIpt
dc.relationUIDP/04427/2020pt
dc.relationAssociation for the Development of Civil Engineering (ACIV)pt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectcyclingpt
dc.subjectperception safetypt
dc.subjectobject detectionpt
dc.subjectimage segmentationpt
dc.subjectroad safetypt
dc.subjectrisk factorspt
dc.titleUsing Deep Learning and Google Street View Imagery to Assess and Improve Cyclist Safety in Londonpt
dc.typearticlept
degois.publication.firstPage10270pt
degois.publication.issue13pt
degois.publication.titleSustainability (Switzerland)pt
dc.peerreviewedyespt
dc.identifier.doi10.3390/su151310270-
degois.publication.volume15pt
dc.date.embargo2023-01-01*
uc.date.periodoEmbargo0pt
item.languageiso639-1en-
item.fulltextCom Texto completo-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.project.grantnoResearch Centre for Territory, Transports and Environment-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.researchunitCITTA - Research Centre for Territory, Transports and Environment-
crisitem.author.researchunitCITTA - Research Centre for Territory, Transports and Environment-
crisitem.author.orcid0000-0003-0502-6472-
crisitem.author.orcid0000-0002-1681-0759-
Appears in Collections:FCTUC Eng.Civil - Artigos em Revistas Internacionais
I&D CITTA - Artigos em Revistas Internacionais
Show simple item record

Page view(s)

129
checked on Oct 30, 2024

Download(s)

51
checked on Oct 30, 2024

Google ScholarTM

Check

Altmetric

Altmetric


This item is licensed under a Creative Commons License Creative Commons