Exploring the impact of driver feedback on safety: A systematic review of studies in real-world driving conditions

IF 3.5 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour Pub Date : 2025-05-31 DOI:10.1016/j.trf.2025.05.028

Armira Kontaxi, Apostolos Ziakopoulos, George Yannis

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引用次数: 0

Abstract

Driver feedback has emerged as a promising tool for improving road safety by influencing driver behavior, propelled by technological advancements. After implementing a PRISMA framework and locating 597 candidate studies, this systematic review selects and examines 34 studies in real-world driving conditions that investigate the role of driver feedback towards improving driving behavior. The review explores the various types of feedback systems used, including smartphone applications, in-vehicle systems, and web-based platforms, and evaluates their effectiveness in reducing risky driving behaviors such as speeding, harsh braking, and mobile phone use. The studies are analyzed in terms of their experimental frameworks, including sample sizes, feedback delivery methods, and feedback phases, along with statistical models employed to assess the impact of feedback on driving behavior and road safety. Additionally, this review highlights research gaps, such as the need to account for traffic conditions and vulnerable road users, while also discussing the potential of emerging technologies like Internet of Things (IoT) and machine learning to enhance the customization and real-time applicability of feedback systems. The findings suggest that driver feedback can significantly reduce crash risks and improve driving behavior, but further research is required to explore its long-term effects and broader applicability.

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探索驾驶员反馈对安全的影响：对现实驾驶条件研究的系统回顾

在技术进步的推动下，驾驶员反馈已经成为一种很有前途的工具，可以通过影响驾驶员的行为来改善道路安全。在实施PRISMA框架并找到597个候选研究后，本系统综述选择并检查了34个真实驾驶条件下的研究，这些研究调查了驾驶员反馈对改善驾驶行为的作用。该报告探讨了所使用的各种反馈系统，包括智能手机应用程序、车载系统和基于网络的平台，并评估了它们在减少超速、急刹车和使用手机等危险驾驶行为方面的有效性。这些研究根据其实验框架进行了分析，包括样本量、反馈方式、反馈阶段，以及用于评估反馈对驾驶行为和道路安全影响的统计模型。此外，本综述还强调了研究差距，例如需要考虑交通状况和弱势道路使用者，同时还讨论了物联网（IoT）和机器学习等新兴技术的潜力，以增强反馈系统的定制和实时适用性。研究结果表明，驾驶员反馈可以显著降低碰撞风险，改善驾驶行为，但需要进一步研究其长期效果和更广泛的适用性。

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来源期刊

Transportation Research Part F-Traffic Psychology and Behaviour Multiple-

CiteScore

7.60

自引率

14.60%

发文量

239

审稿时长

71 days

期刊介绍： Transportation Research Part F: Traffic Psychology and Behaviour focuses on the behavioural and psychological aspects of traffic and transport. The aim of the journal is to enhance theory development, improve the quality of empirical studies and to stimulate the application of research findings in practice. TRF provides a focus and a means of communication for the considerable amount of research activities that are now being carried out in this field. The journal provides a forum for transportation researchers, psychologists, ergonomists, engineers and policy-makers with an interest in traffic and transport psychology.