Abstract：
BACKGROUND: Road traffic deaths are increasing globally, and preventable driving behaviours are a significant cause of these deaths. In-vehicle telematics has been seen as technology that can improve driving behaviour. The technology has been adopted by many insurance companies to track the behaviours of their consumers. This systematic review presents a summary of the ways that in-vehicle telematics has been modelled and analysed.
METHODS: Electronic searches were conducted on Scopus and Web of Science. Studies were only included if they had a sample size of 10 or more participants, collected their data over at least multiple days, and were published during or after 2010. 45 relevant papers were included in the review. 27 of these articles received a rating of \"good\" in the quality assessment.
RESULTS: We found a divide in the literature regarding the use of in-vehicle telematics. Some articles were interested in the utility of in-vehicle telematics for insurance purposes, while others were interested in determining the influence that in-vehicle telematics has on driving behaviour. Machine learning analyses were the most common forms of analysis seen throughout the review, being especially common in articles with insurance-based outcomes. Acceleration, braking, and speed were the most common variables identified in the review.
CONCLUSIONS: We recommend that future studies provide the demographical information of their sample so that the influence of in-vehicle telematics on the driving behaviours of different groups can be understood. It is also recommended that future studies use multi-level models to account for the hierarchical structure of the telematics data. This hierarchical structure refers to the individual trips for each driver.
METHODS: Electronic searches were conducted on Scopus and Web of Science. Studies were only included if they had a sample size of 10 or more participants, collected their data over at least multiple days, and were published during or after 2010. 45 relevant papers were included in the review. 27 of these articles received a rating of \"good\" in the quality assessment.
RESULTS: We found a divide in the literature regarding the use of in-vehicle telematics. Some articles were interested in the utility of in-vehicle telematics for insurance purposes, while others were interested in determining the influence that in-vehicle telematics has on driving behaviour. Machine learning analyses were the most common forms of analysis seen throughout the review, being especially common in articles with insurance-based outcomes. Acceleration, braking, and speed were the most common variables identified in the review.
CONCLUSIONS: We recommend that future studies provide the demographical information of their sample so that the influence of in-vehicle telematics on the driving behaviours of different groups can be understood. It is also recommended that future studies use multi-level models to account for the hierarchical structure of the telematics data. This hierarchical structure refers to the individual trips for each driver.
摘要：
背景：全球道路交通死亡人数正在增加，可预防的驾驶行为是这些死亡的重要原因。车载远程信息处理已被视为可以改善驾驶行为的技术。该技术已被许多保险公司用来跟踪消费者的行为。本系统综述总结了车载远程信息处理的建模和分析方法。
方法：电子搜索在Scopus和WebofScience上进行。如果研究的样本量为10名或更多的参与者，收集了他们至少多天的数据，并在2010年或之后出版。45篇相关论文被纳入审查。其中27篇文章在质量评估中获得了“良好”的评级。
结果：我们在有关车载远程信息处理使用的文献中发现了分歧。一些文章对车载远程信息处理用于保险目的的实用性感兴趣，而其他人则有兴趣确定车载远程信息处理对驾驶行为的影响。机器学习分析是整个评论中最常见的分析形式，在具有基于保险的结果的文章中尤其常见。加速度,制动,和速度是审查中确定的最常见变量。
结论：我们建议未来的研究提供样本的人口统计学信息，以便可以了解车载远程信息处理对不同群体驾驶行为的影响。还建议未来的研究使用多层模型来说明远程信息处理数据的层次结构。该分层结构是指每个驾驶员的单独行程。
方法：电子搜索在Scopus和WebofScience上进行。如果研究的样本量为10名或更多的参与者，收集了他们至少多天的数据，并在2010年或之后出版。45篇相关论文被纳入审查。其中27篇文章在质量评估中获得了“良好”的评级。
结果：我们在有关车载远程信息处理使用的文献中发现了分歧。一些文章对车载远程信息处理用于保险目的的实用性感兴趣，而其他人则有兴趣确定车载远程信息处理对驾驶行为的影响。机器学习分析是整个评论中最常见的分析形式，在具有基于保险的结果的文章中尤其常见。加速度,制动,和速度是审查中确定的最常见变量。
结论：我们建议未来的研究提供样本的人口统计学信息，以便可以了解车载远程信息处理对不同群体驾驶行为的影响。还建议未来的研究使用多层模型来说明远程信息处理数据的层次结构。该分层结构是指每个驾驶员的单独行程。
