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Title: Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction
Authors: Santos, Daniel
Saias, José
Quaresma, Paulo
Nogueira, Vitor
Keywords: machine learning
data analysis
oad accident data
decision trees
random forests
Issue Date: 24-Nov-2021
Publisher: MDPI
Citation: Santos, D.; Saias, J.; Quaresma, P.; Nogueira, V.B. Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction. Computers 2021, 10, 157.
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.
Type: article
Appears in Collections:INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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