Unfolding the dynamics of driving behavior: a machine learning analysis from Germany and Belgium
Abstract The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the al... Mehr ...
| Verfasser: | |
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| Dokumenttyp: | Artikel |
| Erscheinungsdatum: | 2024 |
| Reihe/Periodikum: | European Transport Research Review, Vol 16, Iss 1, Pp 1-13 (2024) |
| Verlag/Hrsg.: |
SpringerOpen
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| Schlagwörter: | On-road field trials / Driving behavior / Long-short-term-memory network (LSTM) / Neural network / Machine learning / Transportation engineering / TA1001-1280 / Transportation and communications / HE1-9990 |
| Sprache: | Englisch |
| Permalink: | https://search.fid-benelux.de/Record/base-28971860 |
| Datenquelle: | BASE; Originalkatalog |
| Powered By: | BASE |
| Link(s) : | https://doi.org/10.1186/s12544-024-00655-z |
Abstract The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the algorithms considered.