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A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and
Sarah Najm Abdulwahid1, Moamin A Mahmoud2, Bilal Bahaa Zaidan3
1College of Graduate Studies, Universiti Tenaga Nasional, Kajang 43000, Malaysia.
Summary
This review analyzes motorcyclist driving behavior research from 2011-2021, identifying gaps in data acquisition, model accuracy, and data types for intelligent transportation systems (ITS). Recommendations are provided for future research.
Area of Science:
- Transportation Engineering
- Human Factors in Driving
- Intelligent Transportation Systems (ITS)
Background:
- Motorcyclist driving behavior is crucial for advancing intelligent transportation systems (ITS).
- Accurate models and datasets are essential for improving road safety and traffic management.
- Real-time sensor data is key for analyzing motorcyclist behavior characteristics.
Purpose of the Study:
- To systematically review and analyze the literature on motorcyclist driving behavior from 2011-2021.
- To identify existing issues, problems, challenges, and research gaps in the field.
- To provide recommendations and research directions for the advancement of motorcyclist behavior studies towards ITS.
Main Methods:
- Systematic literature review of 2214 articles from major digital databases (IEEE Xplore, ScienceDirect, Scopus, Web of Science).
- Two-stage filtration process based on inclusion criteria, focusing on studies of motorcyclist driving style, real-time data acquisition systems (DAS), or questionnaires.
- Categorization of 174 selected articles into reviews, experimental studies, and social studies-based articles.
Main Results:
- Identified key research gaps in data acquisition systems, model accuracy, and the types of data used in proposed models.
- Classified literature into reviews (7/174), experimental studies (41/174), and social studies (126/174) based on experimental conditions.
- Highlighted the need for enhanced methodologies in data collection and analysis for accurate behavior modeling.
Conclusions:
- Significant gaps exist in data acquisition and modeling accuracy for understanding motorcyclist behavior.
- Further research is needed to develop robust data acquisition systems and improve model precision.
- Future directions should focus on integrating advanced data types and methodologies to support ITS development.
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