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Evaluation of Pedestrian Crossing Accidents Using Artificial Neural Network

2025-09-03Book part Open access
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datacite.subject.fosEngenharia e Tecnologia
dc.contributor.authorSantos, Bertha
dc.contributor.authorGonçalves, Jorge
dc.contributor.authorAmin, Shohel
dc.contributor.authorVieira, Sandra Cristina Gil
dc.contributor.authorLopes, Carlos Manuel Valença Martins
dc.date.accessioned2025-10-27T10:17:04Z
dc.date.available2025-10-27T10:17:04Z
dc.date.issued2025-09-03
dc.description.abstractMost of European cities face increasing problems caused by excessive traffic of conventional fuel-based transport modes. To reverse this situation, sustainable urban mobility policies have been promoting soft modes of transport, such as walking. Despite the advantages of walking in reducing traffic congestion and pollution, cities have not always evolved to accommodate the needs of pedestrians. According to the European Commission, in 2020, 20% of road fatalities in the European Union (EU) and 21% in Portugal were pedestrian. Pedestrian fatality rates per million population was 9.7 for all EU countries and 13.1 for Portugal. In European and Portuguese urban areas, 36% and 27% of the fatalities were pedestrians’ and 49% and 56% of all pedestrian fatalities were elderly’s (respectively). In pedestrian infrastructures, crossings are considered the most critical element due to conflicts between vehicles and pedestrians. It is then essential to identify and minimize risk factors that increase the probability of accidents in these locations. The proposed work intends to assess this challenge by using Artificial Neural Network (ANN) to create pedestrian severity prediction models and identify road and pedestrian risk factors for accident occurred in or near urban crossings. The official Portuguese database on run over pedestrian accidents occurred between 2017–2021 was analyzed with ANN considering two scenarios: pre-Covid-19 and during Covid-19 period. Results obtained demonstrate that the use of ANN can promote a proactive infrastructure management, suggesting that crossings traffic lights operation, lighting, shoulders and pavement conditions, high speed limits (51–90 km/h) and pedestrians moving in soft modes are critical factors.por
dc.identifier.citationSantos, B., Gonçalves, J., Amin, S., Vieira, S., Lopes, C. (2026). Evaluation of Pedestrian Crossing Accidents Using Artificial Neural Network. In: McNally, C., Carroll, P., Martinez-Pastor, B., Ghosh, B., Efthymiou, M., Valantasis-Kanellos, N. (eds) Transport Transitions: Advancing Sustainable and Inclusive Mobility. TRAconference 2024. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-031-88974-5_103
dc.identifier.doi10.1007/978-3-031-88974-5_103
dc.identifier.isbn9783031889738
dc.identifier.isbn9783031889745
dc.identifier.issn2196-5544
dc.identifier.issn2196-5552
dc.identifier.urihttp://hdl.handle.net/10400.6/19011
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature Switzerland
dc.relationGeoBioSciences GeoTechnologies and GeoEngineering
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-031-88974-5_103?utm_source=getftr&utm_medium=getftr&utm_campaign=getftr_pilot&getft_integrator=scopus#citeas
dc.relation.ispartofLecture Notes in Mobility
dc.relation.ispartofTransport Transitions: Advancing Sustainable and Inclusive Mobility
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectRoad Safety
dc.subjectPedestrian Accidents at Urban Crossings
dc.subjectArtificial Neural Network (ANN)
dc.subjectSeverity Predictive Model
dc.subjectRisk Factors
dc.titleEvaluation of Pedestrian Crossing Accidents Using Artificial Neural Network
dc.typebook part
dspace.entity.typePublication
oaire.awardTitleGeoBioSciences GeoTechnologies and GeoEngineering
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04035%2F2020/PT
oaire.citation.endPage725
oaire.citation.startPage717
oaire.citation.titleLecture Notes in Mobility
oaire.citation.volumePart F903
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameSantos
person.familyNameGonçalves
person.familyNameAmin
person.familyNameVieira
person.familyNameLopes
person.givenNameBertha
person.givenNameJorge
person.givenNameMd Shohel Reza
person.givenNameSandra Cristina Gil
person.givenNameCarlos Manuel Valença Martins
person.identifier.ciencia-idBE1A-879F-8282
person.identifier.ciencia-idC51C-DF95-FD06
person.identifier.ciencia-id8A1D-7A87-486B
person.identifier.ciencia-id2E19-39E1-C2D0
person.identifier.orcid0000-0002-5545-892X
person.identifier.orcid0000-0001-5369-9693
person.identifier.orcid0000-0002-1726-5887
person.identifier.orcid0000-0003-4156-6388
person.identifier.scopus-author-id54880406000
person.identifier.scopus-author-id23485127000
person.identifier.scopus-author-id55844318600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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