A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations

Abstract

With the continuous emergence of new technologies and the adaptation of smart systems in transportation, motorcyclist driving behaviour plays an important role in the transition towards intelligent transportation systems (ITS). Studying motorcyclist driving behaviour requires accurate models with accurate and complete datasets for better road safety and traffic management. As accuracy is needed in modelling, motorcyclist driving behaviour analyses can be performed using sensors that collect driving behaviour characteristics during real-time experiments. This review article systematically investigates the literature on motorcyclist driving behaviour to present many findings related to the issues, problems, challenges, and research gaps that have existed over the last 10 years (2011–2021). A number of digital databases (i.e., IEEE Xplore®, ScienceDirect, Scopus, and Web of Science) were searched and explored to collect reliable peer-reviewed articles. Out of the 2214 collected articles, only 174 articles formed the final set of articles used in the analysis of the motorcyclist research area. The filtration process consisted of two stages that were implemented on the collected articles. Inclusion criteria were the core of the first stage of the filtration process keeping articles only if they were a study or review written in English or were articles that mainly incorporated the driving style of motorcyclists. The second phase of the filtration process is based on more rules for article inclusion. The criteria of inclusion for the second phase of filtration examined the deployment of motorcyclist driver behaviour characterisation procedures using a real-time-based data acquisition system (DAS) or a questionnaire. The final number of articles was divided into three main groups: reviews (7/174), experimental studies (41/174), and social studies-based articles (126/174). This taxonomy of the literature was developed to group the literature into articles with similar types of experimental conditions. Recommendation topics are also presented to enable and enhance the pace of the development in this research area. Research gaps are presented by implementing a substantial analysis of the previously proposed methodologies. The analysis mainly identified the gaps in the development of data acquisition systems, model accuracy, and data types incorporated in the proposed models. Finally, research directions towards ITS are provided by exploring key topics necessary in the advancement of this research area.

IoT-Based Water Monitoring Systems: A Systematic Review

“…To gain a better understanding of the advantages and disadvantages of any suggested DAS, it is useful to compare (benchmark) several DAS. The selection process can be viewed as a complex challenge that incorporates numerous qualities and various DAS that are documented in the literature to choose the best available DAS [69,70]. There is no clear and valid method for selecting this type of assessment procedure.…”

“…The issues with multi-class classifications that are most frequently mentioned are class separability, class overlaps, and imbalances between and within classes. Multi-class categorization might become severely complex as a result of nonlinear patterns that are not visible in the dataset [70,71].…”

“…The primary question to be answered by the research is whether the developed machine learning-based water assessment models include all of the benchmarking and evaluation criteria during development. Also worth investigating is how developed models manage benchmarking and assessment [69,70].…”

Study of Drivers’ Behavior in Urban Road Traffic

“…As pedestrians are the unprotected road users, it is pedestrians who are at risk of injury or loss of life [7], [8]. Sometimes research is connected only with one group of drivers, like van drivers [9], or motorcycle drivers [10]. Sometimes analysis selects a specific type of road, like in [11] or divides drivers based on age [12].…”

Modeling Motorcyclists’ Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents

“…Driving behavior is the leading cause of traffic fatalities [7,8]. Therefore, preventing aggressive or dangerous driving attitudes is a critical first step toward taking action for decreasing them.…”