Predicting risky driving behavior with classification algorithms: results from a large-scale field-trial and simulator experiment

Road safety is a subject of significant concern and substantially affects individuals across the globe. Thus, real-time, and post-trip interventions have gained significant importance in the past few years. This study aimed to analyze different classification techniques and examine their ability to...

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Vydané v:	European Transport Research Review Ročník 16; číslo 1; s. 65 - 13
Hlavní autori:	Garefalakis, Thodoris, Michelaraki, Eva, Roussou, Stella, Katrakazas, Christos, Brijs, Tom, Yannis, George
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.12.2024 Springer Springer Nature B.V SpringerOpen
Predmet:	Acceleration Accuracy Algorithms Automotive Engineering Cellular telephones Civil Engineering Classification Classification algorithms Driver behavior Driving ability Driving behavior Driving simulator study Engineering Headways Machine learning Machine learning models Multilayer perceptrons Naturalistic driving study Neural networks Original Paper Random forests Real time Regional/Spatial Science Risk factors Robust control Speed limits Support vector machines TRA 2024 Dublin - Transport Transitions (Highlights of the 2024 Transport Research Arena conference) Traffic accidents & safety Traffic safety Transportation Driving behavior Naturalistic driving study Driving simulator study Machine learning models Classification algorithms Random forests
ISSN:	1866-8887, 1867-0717, 1866-8887
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Abstract	Road safety is a subject of significant concern and substantially affects individuals across the globe. Thus, real-time, and post-trip interventions have gained significant importance in the past few years. This study aimed to analyze different classification techniques and examine their ability to identify dangerous driving behavior based on a dual-approach study. The analysis was based on the investigation of important risk factors such as average speed, harsh acceleration, harsh braking, headway, overtaking, distraction (i.e., mobile phone use), and fatigue. In order to achieve the objective of this study, data were collected through a driving simulator as well as a naturalistic driving study. To that end, four classification algorithms, namely support vector machines, random forest (RFs), AdaBoost, and multilayer perceptron (MLP) neural networks were implemented and compared. In the simulator experiment, RFs and MLPs emerged as the top-performing models with an accuracy of 84% and 82%, respectively, demonstrating its ability to accurately classify driving behavior in a controlled environment. In the naturalistic driving study, RF and AdaBoost maintained robust performance, with high accuracy (i.e., 75% and 76.76% respectively) and balanced precision and recall. The outcomes of this study could provide essential guidance for practitioners and researchers on choosing models for driving behavior classification tasks.
AbstractList	Road safety is a subject of significant concern and substantially affects individuals across the globe. Thus, real-time, and post-trip interventions have gained significant importance in the past few years. This study aimed to analyze different classification techniques and examine their ability to identify dangerous driving behavior based on a dual-approach study. The analysis was based on the investigation of important risk factors such as average speed, harsh acceleration, harsh braking, headway, overtaking, distraction (i.e., mobile phone use), and fatigue. In order to achieve the objective of this study, data were collected through a driving simulator as well as a naturalistic driving study. To that end, four classification algorithms, namely support vector machines, random forest (RFs), AdaBoost, and multilayer perceptron (MLP) neural networks were implemented and compared. In the simulator experiment, RFs and MLPs emerged as the top-performing models with an accuracy of 84% and 82%, respectively, demonstrating its ability to accurately classify driving behavior in a controlled environment. In the naturalistic driving study, RF and AdaBoost maintained robust performance, with high accuracy (i.e., 75% and 76.76% respectively) and balanced precision and recall. The outcomes of this study could provide essential guidance for practitioners and researchers on choosing models for driving behavior classification tasks. Abstract Road safety is a subject of significant concern and substantially affects individuals across the globe. Thus, real-time, and post-trip interventions have gained significant importance in the past few years. This study aimed to analyze different classification techniques and examine their ability to identify dangerous driving behavior based on a dual-approach study. The analysis was based on the investigation of important risk factors such as average speed, harsh acceleration, harsh braking, headway, overtaking, distraction (i.e., mobile phone use), and fatigue. In order to achieve the objective of this study, data were collected through a driving simulator as well as a naturalistic driving study. To that end, four classification algorithms, namely support vector machines, random forest (RFs), AdaBoost, and multilayer perceptron (MLP) neural networks were implemented and compared. In the simulator experiment, RFs and MLPs emerged as the top-performing models with an accuracy of 84% and 82%, respectively, demonstrating its ability to accurately classify driving behavior in a controlled environment. In the naturalistic driving study, RF and AdaBoost maintained robust performance, with high accuracy (i.e., 75% and 76.76% respectively) and balanced precision and recall. The outcomes of this study could provide essential guidance for practitioners and researchers on choosing models for driving behavior classification tasks.
ArticleNumber	65
Audience	Academic
Author	Roussou, Stella Yannis, George Michelaraki, Eva Garefalakis, Thodoris Katrakazas, Christos Brijs, Tom
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SubjectTerms	Acceleration Accuracy Algorithms Automotive Engineering Cellular telephones Civil Engineering Classification Classification algorithms Driver behavior Driving ability Driving behavior Driving simulator study Engineering Headways Machine learning Machine learning models Multilayer perceptrons Naturalistic driving study Neural networks Original Paper Random forests Real time Regional/Spatial Science Risk factors Robust control Speed limits Support vector machines TRA 2024 Dublin - Transport Transitions (Highlights of the 2024 Transport Research Arena conference) Traffic accidents & safety Traffic safety Transportation
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Title	Predicting risky driving behavior with classification algorithms: results from a large-scale field-trial and simulator experiment
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