Road accident proneness in Malaysia: A hypothesized model of driver’s demographic profiles, aggressive driving and risky driving

Kassim, Muhammad Asyraf Mohd; Hanan, Suhaila Abdul; Abdullah, Muhammad Safizal; Hong, Chan Pei

doi:10.1063/5.0044555

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the many disadvantages of the devices, they at least offer a complete record regarding day-to-day driving [11]. Even though the quantity of real-time driving studies (as research using this technology is identified) is restricted because of the resources and expenses required, they are becoming more frequent [28]. In human factor studies and intelligent transportation systems, driving behavior analysis is prevalent.…”

Section: Related Studiesmentioning

confidence: 99%

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

Abdulwahid

Mahmoud

Ibrahim

et al. 2022

IJERPH

View full text Add to dashboard Cite

Driving behavior is considered one of the most important factors in all road crashes, accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the leading cause of traffic accidents that jeopardize human life and property. By evaluating data collected by various collection devices, it is possible to detect dangerous and aggressive driving, which is a huge step toward altering the situation. The utilization of driving data, which has arisen as a new tool for assessing the style of driving, has lately moved the concentration of aggressive recognition research. The goal of this study is to detect dangerous and aggressive driving profiles utilizing data gathered from motorcyclists and smartphone APPs that run on the Android operating system. A two-stage method is used: first, determine driver profile thresholds (rules), then differentiate between non-aggressive and aggressive driving and show the harmful conduct for producing the needed outcome. The data were collected from motorcycles using -Speedometer GPS-, an application based on the Android system, supplemented with spatiotemporal information. After the completion of data collection, preprocessing of the raw data was conducted to make them ready for use. The next steps were extracting the relevant features and developing the classification model, which consists of the transformation of patterns into features that are considered a compressed representation. Lastly, this study discovered a collection of key characteristics which might be used to categorize driving behavior as aggressive, normal, or dangerous. The results also revealed major safety issues related to driving behavior while riding a motorcycle, providing valuable insight into improving road safety and reducing accidents.

show abstract

Section: Related Studiesmentioning

confidence: 99%