Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context

Detalhes bibliográficos
Autor(a) principal: Alpalhão, Nuno
Data de Publicação: 2022
Outros Autores: Sarmento, Pedro, Pinheiro, Flávio L., Tremoceiro, João, Neto, Miguel de Castro
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/130795
Resumo: Alpalhão, N., Sarmento, P., Pinheiro, F. L., Tremoceiro, J., & Neto, M. D. C. (2022). Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context. In Livro de Resumos da Conferência do Projeto de Investigação Científica “Fatores de Transformação Urbana (DRIVIT-UP)” em conjunto com I Conferência sobre Ciência de Dados para Ciências Sociais e VI Conferência de Planeamento Regional e Urbano. [Abstract book from the Conference of the Scientific Research Project “Drivers of urban transformation (DRIVIT-UP)” a joitly event with I Conference on Data Science for the Social Sciences And VI Conference on Regional and Urban Planning] (pp. 52-55). UA Editora. https://doi.org/10.48528/pkzd-wz70
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spelling Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban contextTraffic accidentsUrban PlanningNeural NetworksGradient Boosting FrameworkSimulationSDG 3 - Good Health and Well-beingAlpalhão, N., Sarmento, P., Pinheiro, F. L., Tremoceiro, J., & Neto, M. D. C. (2022). Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context. In Livro de Resumos da Conferência do Projeto de Investigação Científica “Fatores de Transformação Urbana (DRIVIT-UP)” em conjunto com I Conferência sobre Ciência de Dados para Ciências Sociais e VI Conferência de Planeamento Regional e Urbano. [Abstract book from the Conference of the Scientific Research Project “Drivers of urban transformation (DRIVIT-UP)” a joitly event with I Conference on Data Science for the Social Sciences And VI Conference on Regional and Urban Planning] (pp. 52-55). UA Editora. https://doi.org/10.48528/pkzd-wz70Traffic accidents are the cause of considerable losses both in property and in human lives, as they can result in economic problems to the people involved and to society, in injury, incapacity and even death. To reduce and minimize these disastrous effects, it is important that emergency services have the ability to plan and define strategies to reduce the time taken to provide a first aid response to affected individuals. In this sense, traffic accident risk prediction can play a crucial role in the definition of these strategies, as it allows to both understand the factors that influence the occurrence of traffic accidents and, to anticipate in space and time in which location it is more likely that traffic accident occur. Several studies have been developed in regard to traffic accident prediction, such as Poisson’s and binomial negative algorithms (Fancello, Soddu, & Fadda, 2018), ARIMA models (Ihueze & Onwurah, 2018), machine learning techniques like regression models (Chang & Chen, 2005), K-Nearest Neighbour (KNN), Bayesian networks (Hossain & Muromachi, 2012) and decision trees (Lin, Wang, & Sadek, 2015). Moreover, some deep learning approaches (Chen, Song, Yamada, & Shibasaki, 2016; Ren, Song, Wang, Hu, & Lei, 2018) have been developed to estimate the risk of traffic accidents, but in coarser regular spatial grids, failing to provide the necessary spatial detail needed for emergency operations. Besides this aspect, most of the studies regarding prediction of traffic accidents are made in a non-urban context and not enough attention has been provided to the prediction of traffic accident risk in urban environments (Yu et al., 2021). In this paper we have developed and tested two traffic accident probability prediction models based on neural networks architectures and a gradient boosting framework that uses tree-based learning algorithms. For this purpose, we used information regarding traffic accidents occurrences, that required firefighters’ intervention, in the city of Lisbon from 2013 to 2020. Traffic accidents occurrences were aggregated at the road level by period of day, along with road characteristics data, available on Lisbon Open Data Portal, and weather information. Naturally, there are far more periods without accidents than with, to deal with this unbalanced data, the modelling strategy was divided in two main steps, in which the first one consisted in a classification to identify periods where the probability of having traffic accidents was different than zero. From the resulting sample of the first step, a regression was used to compute the probability of traffic accidents by period of day at street level. The tested models provided good estimates for both the neural network and tree-based learning algorithms. From the results, a traffic accident risk simulator was developed, allowing the re-assessment of the risk of traffic accidents, if street characteristics and weather conditions are changed for a specific street and period of day. This simulator provides to the emergency services, an essential tool for planning and management of emergency operations. This work was supported by the Connecting Europe Facility (CEF) – Telecommunications sector in the framework of project Urban Co-Creation Data Lab [INEA/CEF/ICT/A2018/1837945].UA EditoraNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAlpalhão, NunoSarmento, PedroPinheiro, Flávio L.Tremoceiro, JoãoNeto, Miguel de Castro2022-01-13T23:24:25Z2022-01-012022-01-01T00:00:00Zbook partinfo:eu-repo/semantics/publishedVersion5application/pdfhttp://hdl.handle.net/10362/130795eng978-972-789-727-8PURE: 36078886https://doi.org/10.48528/pkzd-wz70info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T17:58:08Zoai:run.unl.pt:10362/130795Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:58:08Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context
title Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context
spellingShingle Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context
Alpalhão, Nuno
Traffic accidents
Urban Planning
Neural Networks
Gradient Boosting Framework
Simulation
SDG 3 - Good Health and Well-being
title_short Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context
title_full Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context
title_fullStr Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context
title_full_unstemmed Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context
title_sort Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context
author Alpalhão, Nuno
author_facet Alpalhão, Nuno
Sarmento, Pedro
Pinheiro, Flávio L.
Tremoceiro, João
Neto, Miguel de Castro
author_role author
author2 Sarmento, Pedro
Pinheiro, Flávio L.
Tremoceiro, João
Neto, Miguel de Castro
author2_role author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Alpalhão, Nuno
Sarmento, Pedro
Pinheiro, Flávio L.
Tremoceiro, João
Neto, Miguel de Castro
dc.subject.por.fl_str_mv Traffic accidents
Urban Planning
Neural Networks
Gradient Boosting Framework
Simulation
SDG 3 - Good Health and Well-being
topic Traffic accidents
Urban Planning
Neural Networks
Gradient Boosting Framework
Simulation
SDG 3 - Good Health and Well-being
description Alpalhão, N., Sarmento, P., Pinheiro, F. L., Tremoceiro, J., & Neto, M. D. C. (2022). Prediction and simulation of the risk of traffic accidents using neural networks and gradient boosting with an hybrid classification/regression modelling approach in urban context. In Livro de Resumos da Conferência do Projeto de Investigação Científica “Fatores de Transformação Urbana (DRIVIT-UP)” em conjunto com I Conferência sobre Ciência de Dados para Ciências Sociais e VI Conferência de Planeamento Regional e Urbano. [Abstract book from the Conference of the Scientific Research Project “Drivers of urban transformation (DRIVIT-UP)” a joitly event with I Conference on Data Science for the Social Sciences And VI Conference on Regional and Urban Planning] (pp. 52-55). UA Editora. https://doi.org/10.48528/pkzd-wz70
publishDate 2022
dc.date.none.fl_str_mv 2022-01-13T23:24:25Z
2022-01-01
2022-01-01T00:00:00Z
dc.type.driver.fl_str_mv book part
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/130795
url http://hdl.handle.net/10362/130795
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-972-789-727-8
PURE: 36078886
https://doi.org/10.48528/pkzd-wz70
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 5
application/pdf
dc.publisher.none.fl_str_mv UA Editora
publisher.none.fl_str_mv UA Editora
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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