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| Seuring, Stefan |
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| Nor Azizi, S. |
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| Pato, Margarida Vaz |
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| Kölker, Katrin |
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| Huber, Oliver |
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| Király, Tamás |
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| Spengler, Thomas Stefan |
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| Al-Ammar, Essam A. |
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| Dargahi, Fatemeh |
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| Mota, Rui |
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| Mazalan, Nurul Aliah Amirah |
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| Macharis, Cathy | Brussels |
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| Arunasari, Yova Tri |
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| Nunez, Alfredo | Delft |
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| Bouhorma, Mohammed |
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| Bonato, Matteo |
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| Fitriani, Ira |
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| Autor Correspondente Coelho, Sílvia. |
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| Pond, Stephen |
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| Okwara, Ukoha Kalu |
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| Toufigh, Vahid |
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| Campisi, Tiziana | Enna |
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| Ermolieva, Tatiana |
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| Sánchez-Cambronero, Santos |
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| Agzamov, Akhror |
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Ziakopoulos, Apostolos
in Cooperation with on an Cooperation-Score of 37%
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Publications (21/21 displayed)
- 2023A Review of Surrogate Safety Measures Uses in Historical Crash Investigationscitations
- 2023The LEVITATE Policy Support Tool of Connected and Automated Transport Systemscitations
- 2023COVID-19 and Driving Behavior: Which Were the Most Crucial Influencing Factors?citations
- 2023From conflicts to crashes: Simulating macroscopic connected and automated driving vehicle safetycitations
- 2023Exploring speeding behavior using naturalistic car driving data from smartphonescitations
- 2023Comparing Machine Learning Techniques for Predictions of Motorway Segment Crash Risk Levelcitations
- 2023The impacts of automated urban delivery and consolidation
- 2023Exploiting Surrogate Safety Measures and Road Design Characteristics towards Crash Investigations in Motorway Segmentscitations
- 2022Spatial predictions of harsh driving events using statistical and machine learning methodscitations
- 2022The impacts of automated urban delivery and consolidation
- 2021Modelling self-reported driver perspectives and fatigued driving via deep learningcitations
- 2021Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecastingcitations
- 2021To cross or not to cross? Review and meta-analysis of pedestrian gap acceptance decisions at midblock street crossingscitations
- 2021Examining the relationship between impaired driving and past crash involvement in Europe: Insights from the ESRA studycitations
- 2021Investigation of the speeding behavior of motorcyclists through an innovative smartphone applicationcitations
- 2021Predicting fatigued driving via deep learning based on driver perspectives
- 2020Investigation of the effect of tourism on road crashescitations
- 2019A systematic cost-benefit analysis of 29 road safety measurescitations
- 2019Review and ranking of crash risk factors related to the road infrastructurecitations
- 2016Identification of Road User related Risk Factors. SAfetyCube Deliverable 4.1
- 2016Identification of Road User related Risk Factors. SAfetyCube Deliverable 4.1
Places of action
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article
Predicting fatigued driving via deep learning based on driver perspectives
Abstract
Driving while fatigued is a considerably understudied risk factor contributing to car crashes every year. The first step in mitigating the respective crash risks is to attempt to infer fatigued driving from other parameters, in order to gauge its extend in road networks. The aim of this study is to investigate the extent to which declared fatigued driving behavior can be predicted based on overall driver opinions and perceptions on that issue. For that purpose, a broad cross-country questionnaire from the ESRA2 survey was used. The questionnaire is related to self-declared beliefs, perception, and attitudes towards a wide range of traffic safety topics. Initially, a binary logistic regression model was trained to provide causal insights on which variables affect the likelihood that a driver engaged in driving while fatigued. Drivers reporting driving under the influence of drugs, fatigue, or alcohol, as well as speeding, safety, and texting while driving or drivers who were more acceptable of fatigued driving were more likely to have recently driven while fatigued. In contrast, acceptability of other hazardous behaviors, namely mobile phone use and drunk driving, was negatively correlated with fatigued driving behavior, as were more responsible driver perspectives overall. To provide a more accurate detection mechanism, which would also incorporate non-linear effects, a Deep Neural Network (DNN) was subsequently trained on the data, slightly outperforming the binary logistic model. From the results of both models, it was concluded that declared fatigued driving behavior can be predicted from questionnaire data, providing new insights to fatigue detection.
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