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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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Filtness, Ashleigh
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (20/20 displayed)
- 2023Framework for behaviour change implemented in real-time and post-trip interventions of the H2020 i-DREAMS naturalistic driving project
- 2023State-of-the-art Technologies for Post-Trip Safety Interventions
- 2023Effectiveness of real-time and post-trip interventions from the H2020 i-DREAMS naturalistic driving project: A Sneak Preview
- 2022Investigating the effects of sleepiness in truck drivers on their headway: an instrumental variable model with grouped random parameters and heterogeneity in their meanscitations
- 2022Methodology for the Evaluation of Safety Interventions
- 2022Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their meanscitations
- 2021Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countriescitations
- 2021The i-DREAMS intervention strategies to reduce driver fatigue and sleepiness for different transport modescitations
- 2021Post-trip safety interventions: State-of-the-art, challenges, and practical implicationscitations
- 2021Modelling driver decision-making at railway level crossings using the abstraction decomposition spacecitations
- 2019Riding the emotional roller-coaster: Using the circumplex model of affect to model motorcycle riders’ emotional state-changes at intersectionscitations
- 2019What do driver educators and young drivers think about driving simulators? A qualitative draw-and-talk studycitations
- 2019Riding the emotional roller-coastercitations
- 2019The effect of psychosocial factors on perceptions of driver education using the goals for driver education frameworkcitations
- 2019Review and ranking of crash risk factors related to the road infrastructurecitations
- 2018A mixed-methods study of driver education informed by the Goals for Driver Education: Do young drivers and educators agree on what was taught?citations
- 2018Serious Road Traffic Injuries in Europe, Lessons from the EU Research Project SafetyCubecitations
- 2018Burden of injury of serious road injuries in six EU countriescitations
- 2016Identification of Road User related Risk Factors. SAfetyCube Deliverable 4.1
- 2016Identification of Road User related Risk Factors. SAfetyCube Deliverable 4.1
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article
Post-trip safety interventions: State-of-the-art, challenges, and practical implications
Abstract
Introduction: Currently, risky driving behaviour is a major contributor to road crashes and as a result, wide array of tools have been developed in order to record and improve driving behaviour. Within that group of tools, interventions have been indicated to significantly enhance driving behaviour and road safety. This study critically reviews monitoring technologies that provide post-trip interventions, such as retrospective visual feedback, gamification, rewards or penalties, in order to inform an appropriate driver mentoring strategy delivered after each trip. Method: The work presented here is part of the European Commission H2020 i-DREAMS project. The reviewed platform characteristics were obtained through commercially available solutions as well as a comprehensive literature search in popular scientific databases , such as Scopus and Google Scholar. Focus was given on state-of-the-art-technologies for post-trip interventions utilized in four different transport modes (i.e. car, truck, bus and rail) associated with risk prevention and mitigation. Results: The synthesized results revealed that smartphone applications and web-based platforms are the most accepted, frequently and easiest to use tools in cars, buses and trucks across all papers considered, while limited evidence of post-trip interventions in-rail was found. The majority of smartphone applications detected mobile phone use and harsh events and provided individual performance scores, while in-vehicle systems provided delayed visual reports through a web-based platform. Conclusions: Gamification and appropriate rewards appeared to be effective solutions, as it was found that they keep drivers motivated in improving their driving skills, but it was clear that these cannot be performed in isolation and a combination with other strategies (i.e. driver coaching and support) might be beneficial. Nevertheless, as there is no holistic and cross-modal post-trip intervention solution developed in real-world environments, challenges associated with ...
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