Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents’ number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications.

中文翻译：

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用于改善驾驶行为的游戏化移动应用程序：系统映射研究

许多研究工作和官方报告都认为，道路上不负责任的驾驶行为是导致事故的主要原因。因此，负责任的驾驶行为可以显着减少事故的数量和严重程度。因此，在研究领域和工业领域，移动技术被广泛用于帮助驾驶员降低事故率和预防事故。例如，提供了几个移动应用程序来帮助司机改善他们的驾驶行为。最近，由于移动云计算，智能手机可以从云中服务器的计算能力中受益，以执行机器学习算法。因此，许多驾驶辅助和控制的移动应用程序都基于机器学习技术，以根据驾驶员历史记录、上下文、和简介。此外，游戏化是这些移动应用程序设计中的一个关键元素，它允许驾驶员培养参与度和动机以改善他们的驾驶行为。为了概述现有的可改善驾驶行为的移动应用程序，我们选择对最常见的移动应用程序存储库中存在的或作为研究成果在数字图书馆中发布的驾驶行为移动应用程序进行系统映射研究。特别是，我们应该探索它们的功能、收集的数据类型、使用的游戏化元素以及使用的机器学习技术和算法。我们已成功识别出 220 款有助于改善驾驶行为的移动应用程序。在这项工作中，