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.
Highlights
This work was conducted as part of the MOPREVIS (Modeling and Prediction of RoadAccidents in the District of Setúbal) project
MOPREVIS [1] is a project of the University of Évora in partnership with the Territorial Command of the GNR
The aim is to figure out what factors increase the likelihood of accidents and the severity of those accidents, develop predictive models for both the number and severity of accidents, and test a predictive model to predict the likelihood of accidents on specific road segments
Summary
This work was conducted as part of the MOPREVIS Accidents in the District of Setúbal) project. MOPREVIS [1] is a project of the University of Évora in partnership with the Territorial Command of the GNR The project’s primary goal is to reduce serious accidents in the Setúbal district, which, in 2017, despite not having the highest number of accidents, has the highest number of fatalities. The project is only using data from the district of Setúbal, but the plan is to expand it to other districts in the future. Road traffic accidents are one of the most lethal hazards to people. Predicting potential traffic accidents can help to avoid them, decrease damage from them, give drivers alerts to potential dangers, or improve the emergency management system. A reduction in reaction time may be attained if authorities in an area receive advance notice or warning as to which portions of the district’s roads are more likely to have an accident at various times of the day
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