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Title: | Predicting risky driving behavior with classification algorithms: results from a large-scale field-trial and simulator experiment | Authors: | Garefalakis, Thodoris Michelaraki, Eva Roussou, Stella Katrakazas, Christos BRIJS, Tom Yannis, George |
Issue Date: | 2024 | Publisher: | SPRINGER | Source: | European transport research review (Print), 16 (1) (Art N° 65) | 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 multi-layer 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. | Keywords: | Driving behavior;Random forests;Machine learning models;Classification algorithms;Driving simulator study;Naturalistic driving study | Document URI: | http://hdl.handle.net/1942/44771 | ISSN: | 1867-0717 | e-ISSN: | 1866-8887 | DOI: | 10.1186/s12544-024-00691-9 | ISI #: | 001359466700001 | Rights: | The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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Predicting risky driving behavior with classification algorithms_ results from a large-scale field-trial and simulator experiment.pdf | Published version | 1.21 MB | Adobe PDF | View/Open |
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