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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Veröffentlicht in:	European Transport Research Review Jg. 16; H. 1; S. 65 - 13
Hauptverfasser:	Garefalakis, Thodoris, Michelaraki, Eva, Roussou, Stella, Katrakazas, Christos, Brijs, Tom, Yannis, George
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2024 Springer Springer Nature B.V SpringerOpen
Schlagworte:	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
Online-Zugang:	Volltext
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