Add abstract
Want to add your dissertation abstract to this database? It only takes a minute!
Search abstract
Search for abstracts by subject, author or institution
Abstracts Transportation
C-ITS based prediction of driver red light running and turning behaviours
by Md Mostafizur Rahman Komol
| Institution: | Queensland University of Technology |
|---|---|
| Department: | |
| Degree: | |
| Year: | 2022 |
| Keywords: | Intelligent Transportation System; Connected Vehicle; Machine Learning; Long Short Term Memory Network; Gated Recurrent Unit Network |
| Posted: | 3/25/2025 |
| Record ID: | 2309792 |
| Full text PDF: | https://eprints.qut.edu.au/227694/ |
Abstract
Red light running is a major traffic violation. Drivers often aggressively or unintentionally violate red signal and cause traffic collisions. Moreover, Vision impairment of turning vehicles by large vehicles and road side static structures near intersections often lead to VRU crashes during their crossing at the intersection. In this research, we have developed models to predict drivers’ red light running and turning behaviour at intersections using Long Short Term Memory and Gated Recurrent Unit algorithms. We have used vehicle kinematic dataset of the C-ITS project: Ipswich Connected Vehicle Pilot, Queensland, taken from the Department of Transport and Main Road, Queensland.
Add abstract
Want to add your dissertation abstract to this database? It only takes a minute!
Search abstract
Search for abstracts by subject, author or institution
