Comprehensive driver behaviour review: Taxonomy, issues and challenges, motivations and research direction towards achieving a smart transportation environment. (May 2022)

• Record Type: Journal Article
• Authors: Zaidan, R.A.; Alamoodi, A.H.; Zaidan, B.B.; Zaidan, A.A.; Albahri, O.S.; Talal, Mohammed; Garfan, Salem; Sulaiman, Suliana; Mohammed, Ali; Kareem, Z.H.; Malik, R.Q.; Ameen, H.A.
• Is Part Of: Engineering applications of artificial intelligence. Volume 111(2022)
• Publication Date: 2022-05
• Subjects: Data exchange -- Driver behaviour -- Communication -- Sensor; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104745
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The aim of this article is to review and analyse previous academic articles associated with car behaviour analysis for the period of 2010 to June 10, 2021 and understand the benefits of using data collection devices. Articles related to car driver behaviour and sensor utilisation are systematically searched. Three major databases – ScienceDirect, IEEE Xplore and Web of Science – were searched. A set of inclusion and exclusion criteria were developed for the search protocol. All articles were coherently classified via taxonomy. Also. The motives that have led researchers to continue their investigations are explored. The challenges and issues of driver behaviour analysis are illustrated with respect to power consumption, data analysis, detection, cost, security and privacy, sensor usage and individual challenges. The research direction of this review points towards different aspects based on the critical analysis of the different scenarios of driver behaviour studies in real time situations. Here, the critical behaviour analysis of intelligent transportation system development is addressed. The gaps in the reviewed articles include the following: sensors used during experiments, the effect of thresholds on labelling processes or data balancing and classification accuracy, the thresholds in identifying driving styles in the car-following model, insufficient experiment size (large scale or small scale) and limitations in data pre-processing. An implementation map depicting the steps of the case study is provided to give insights into the procedures and the problems they address. This review is expected to offer valid and clear points, contributing to the enhancement of driver behaviour research.