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Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study

Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to in...

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Autores principales: Zahid, Muhammad, Chen, Yangzhou, Jamal, Arshad, Al-Ofi, Khalaf A., Al-Ahmadi, Hassan M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400276/
https://www.ncbi.nlm.nih.gov/pubmed/32708404
http://dx.doi.org/10.3390/ijerph17145193
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author Zahid, Muhammad
Chen, Yangzhou
Jamal, Arshad
Al-Ofi, Khalaf A.
Al-Ahmadi, Hassan M.
author_facet Zahid, Muhammad
Chen, Yangzhou
Jamal, Arshad
Al-Ofi, Khalaf A.
Al-Ahmadi, Hassan M.
author_sort Zahid, Muhammad
collection PubMed
description Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to investigate different traffic violations (overspeeding, wrong-way driving, illegal parking, non-compliance traffic control devices, etc.) using spatial analysis and different machine learning methods. Georeferenced violation data along two expressways (S308 and S219) for the year 2016 was obtained from the traffic police department, in the city of Luzhou, China. Detailed descriptive analysis of the data showed that wrong-way driving was the most common violation type observed. Inverse Distance Weighted (IDW) interpolation in the ArcMap Geographic Information System (GIS) was used to develop violation hotspots zones to guide on efficient use of limited resources during the treatment of high-risk sites. Lastly, a systematic Machine Learning (ML) framework, such as K Nearest Neighbors (KNN) models (using k = 3, 5, 7, 10, and 12), support vector machine (SVM), and CN2 Rule Inducer, was utilized for classification and prediction of each violation type as a function of several explanatory variables. The predictive performance of proposed ML models was examined using different evaluation metrics, such as Area Under the Curve (AUC), F-score, precision, recall, specificity, and run time. The results also showed that the KNN model with k = 7 using manhattan evaluation had an accuracy of 99% and outperformed the SVM and CN2 Rule Inducer. The outcome of this study could provide the practitioners and decision-makers with essential insights for appropriate engineering and traffic control measures to improve the safety of road-users.
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spelling pubmed-74002762020-08-23 Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study Zahid, Muhammad Chen, Yangzhou Jamal, Arshad Al-Ofi, Khalaf A. Al-Ahmadi, Hassan M. Int J Environ Res Public Health Article Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to investigate different traffic violations (overspeeding, wrong-way driving, illegal parking, non-compliance traffic control devices, etc.) using spatial analysis and different machine learning methods. Georeferenced violation data along two expressways (S308 and S219) for the year 2016 was obtained from the traffic police department, in the city of Luzhou, China. Detailed descriptive analysis of the data showed that wrong-way driving was the most common violation type observed. Inverse Distance Weighted (IDW) interpolation in the ArcMap Geographic Information System (GIS) was used to develop violation hotspots zones to guide on efficient use of limited resources during the treatment of high-risk sites. Lastly, a systematic Machine Learning (ML) framework, such as K Nearest Neighbors (KNN) models (using k = 3, 5, 7, 10, and 12), support vector machine (SVM), and CN2 Rule Inducer, was utilized for classification and prediction of each violation type as a function of several explanatory variables. The predictive performance of proposed ML models was examined using different evaluation metrics, such as Area Under the Curve (AUC), F-score, precision, recall, specificity, and run time. The results also showed that the KNN model with k = 7 using manhattan evaluation had an accuracy of 99% and outperformed the SVM and CN2 Rule Inducer. The outcome of this study could provide the practitioners and decision-makers with essential insights for appropriate engineering and traffic control measures to improve the safety of road-users. MDPI 2020-07-18 2020-07 /pmc/articles/PMC7400276/ /pubmed/32708404 http://dx.doi.org/10.3390/ijerph17145193 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zahid, Muhammad
Chen, Yangzhou
Jamal, Arshad
Al-Ofi, Khalaf A.
Al-Ahmadi, Hassan M.
Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study
title Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study
title_full Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study
title_fullStr Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study
title_full_unstemmed Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study
title_short Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study
title_sort adopting machine learning and spatial analysis techniques for driver risk assessment: insights from a case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400276/
https://www.ncbi.nlm.nih.gov/pubmed/32708404
http://dx.doi.org/10.3390/ijerph17145193
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