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

Muhammad Zahid,Yangzhou Chen,Arshad Jamal,Khalaf A Al-Ofi,Hassan M Al-Ahmadi

doi:10.3390/ijerph17145193

Abstract

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.

Highlights

Road transport is considered the backbone of the nation’s economy
Framework, including Support Vector Machine (SVM), CN2 Rule Inducer, and K Nearest Neighbors (KNN) to classify and predict traffic violations considering a number of available explanatory variables; (iii) performed comprehensive comparative analysis for proposed Machine Learning (ML) algorithms based on several classification evaluation metrics; and (iv) our results showed that KNN outperformed other models
Traffic violations experienced by different vehicles, like private cars, taxis, vans, buses, and small trucks, were included in this analysis since they hold a large proportion of total occurrences of violations

Summary

Introduction

Road transport is considered the backbone of the nation’s economy. In China, rapid economic growth during the past three decades has brought a revolution in the transportation industry.The motorization rate has witnessed exponential growth, in urban areas. Road transport is considered the backbone of the nation’s economy. In China, rapid economic growth during the past three decades has brought a revolution in the transportation industry. The motorization rate has witnessed exponential growth, in urban areas. Though this rapid expansion of urban transport infrastructure has inarguable benefits for various businesses, it has caused serious agony in the form of extreme traffic congestion, limited parking facilities, increases air pollution, and noise pollution, as well as safety concerns. Res. Public Health 2020, 17, 5193; doi:10.3390/ijerph17145193 www.mdpi.com/journal/ijerph

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