Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Daniel Santos; José Saias; Paulo Quaresma; Vítor Beires Nogueira

doi:10.3390/computers10120157

journal article Open Access Nov 24, 2021

Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Daniel Santos

José Saias

Paulo Quaresma

Vítor Beires Nogueira

Computers Vol. 10 No. 12 pp. 157 · MDPI AG

View at Publisher Save 10.3390/computers10120157

Abstract

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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References

Details

Published: Nov 24, 2021
Vol/Issue: 10(12)
Pages: 157
License: View

Authors

D

Daniel Santos

Informatics Departament, University of Évora, 7002-554 Évora, Portugal

J

José Saias

Informatics Departament, University of Évora, 7002-554 Évora, Portugal

P

Paulo Quaresma

Informatics Departament, University of Évora, 7002-554 Évora, Portugal

V

Vítor Beires Nogueira

Informatics Departament, University of Évora, 7002-554 Évora, Portugal

Funding

Fundação para a Ciência e Tecnologia Award: FCT DSAIPA/DS/0090/2018

Cite This Article

Daniel Santos, José Saias, Paulo Quaresma, et al. (2021). Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction. Computers, 10(12), 157. https://doi.org/10.3390/computers10120157

Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

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