@article {ElHafidy2021,
	title = {Gamified mobile applications for improving driving behavior: A systematic mapping study},
	journal = {Mobile Information Systems},
	volume = {2021},
	year = {2021},
	note = {cited By 4},
	abstract = {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{\textquoteright} 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. {\textcopyright} 2021 Abderrahim El hafidy et al.},
	keywords = {Accidents, Computing power, Digital libraries, Driving assistance, Driving behavior, Gamification, Industrial area, Industrial research, Learning algorithms, Machine learning, Machine learning techniques, Mapping, Mobile applications, Mobile cloud computing, Mobile computing, Mobile Technology, Systematic mapping studies},
	doi = {10.1155/2021/6677075},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114117887\&doi=10.1155\%2f2021\%2f6677075\&partnerID=40\&md5=6ad1b624deabb49917039716eb133f13},
	author = {El Hafidy, A. and Rachad, T. and Idri, A. and Zellou, A.}
}
