Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction
Main Author: | |
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Publication Date: | 2021 |
Other Authors: | , , |
Format: | Article |
Language: | por |
Source: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Download full: | http://hdl.handle.net/10174/32115 https://doi.org/10.3390/computers10120157 |
Summary: | Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots. |
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Machine Learning Approaches to Traffic Accident Analysis and Hotspot Predictionmachine learningdata analysisoad accident dataclusteringdecision treesrandom forestsTraffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.FCT Fundação para a Ciência e a Tecnologia, under the project with reference FCT DSAIPA/DS/0090/2018, “MOPREVIS—Modelação e Predição de Acidentes de Viação no Distrito de Setúbal”.MDPI2022-05-30T11:00:32Z2022-05-302021-11-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/32115http://hdl.handle.net/10174/32115https://doi.org/10.3390/computers10120157porSantos, D.; Saias, J.; Quaresma, P.; Nogueira, V.B. Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction. Computers 2021, 10, 157.https://www.mdpi.com/2073-431X/10/12/157/htmdfsantos@uevora.ptjsaias@uevora.ptpq@uevora.ptvbn@uevora.pt283Santos, DanielSaias, JoséQuaresma, PauloNogueira, Vitorinfo: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:RCAAP2024-01-03T19:32:30Zoai:dspace.uevora.pt:10174/32115Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:21:12.595229Repositório 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 |
Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction |
title |
Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction |
spellingShingle |
Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction Santos, Daniel machine learning data analysis oad accident data clustering decision trees random forests |
title_short |
Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction |
title_full |
Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction |
title_fullStr |
Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction |
title_full_unstemmed |
Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction |
title_sort |
Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction |
author |
Santos, Daniel |
author_facet |
Santos, Daniel Saias, José Quaresma, Paulo Nogueira, Vitor |
author_role |
author |
author2 |
Saias, José Quaresma, Paulo Nogueira, Vitor |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Santos, Daniel Saias, José Quaresma, Paulo Nogueira, Vitor |
dc.subject.por.fl_str_mv |
machine learning data analysis oad accident data clustering decision trees random forests |
topic |
machine learning data analysis oad accident data clustering decision trees random forests |
description |
Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-24T00:00:00Z 2022-05-30T11:00:32Z 2022-05-30 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/32115 http://hdl.handle.net/10174/32115 https://doi.org/10.3390/computers10120157 |
url |
http://hdl.handle.net/10174/32115 https://doi.org/10.3390/computers10120157 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
Santos, D.; Saias, J.; Quaresma, P.; Nogueira, V.B. Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction. Computers 2021, 10, 157. https://www.mdpi.com/2073-431X/10/12/157/htm dfsantos@uevora.pt jsaias@uevora.pt pq@uevora.pt vbn@uevora.pt 283 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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MDPI |
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MDPI |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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