Prof Dr. Shimul Haque is passionate about road safety engineering research. He is a specialist in econometrics and artificial intelligence applications in transport engineering and traffic safety.
Dr. Hassan Bin Tahir is a Post Doctoral Research Fellow in transportation engineering at the Queensland University of Technology (QUT). He completed his PhD in Transportation Engineering from QUT.
Dr. Zili is a research fellow. His works are related to statistical modeling and its application in transport engineering. His research interests include forecasting, Bayesian models, numerical computation, etc.
The PhD project aims to develop next generation models for crash risk estimation in real-time. Leveraging the combined benefits of Intelligent Transport Systems and computer-vision techniques, traffic conflicts.
Howlader is a research student at Science & Engineering Faculty, Queensland University of Technology (QUT). His current focus is on conflict data-based crash prediction methodologies.
Shinthia is a PhD candidate in the Faculty of Engineering at Queensland University of Technology (QUT) under the award of the Prime Minister Fellowship of Bangladesh.
Tasmin is a PhD student at the School of Faculty of Engineering, Queensland University of Technology (QUT). She completed her MPhil from the Swinburne University of Technology, Melbourne, Australia
A New Road User Safety Field Theory for Pedestrian Safety Assessment at Signalized Intersections
Shubham is a PhD candidate at Queensland University of Technology's (QUT) School of Civil and Environmental Engineering.
Md Eaysir Arafat is currently pursuing PhD degree in the School of Civil and Environmental Engineering at Queensland University of Technology (QUT).
Sunny is a PhD student at the School of Faculty of Engineering, Queensland University of Technology (QUT). He completed his MPhill from QUT.
Dr. Ali’s PhD dissertation developed a Complete LAne-Changing Decision (CLACD) modelling framework that explains both the mandatory and discretionary lane-changing behaviours and describes lane-changing behaviours in both the traditional and connected environments.
Dr. Getu Segni Tulu’s PhD thesis focuses on pedestrian safety in Ethiopia, where 50-60% of traffic fatalities are pedestrians. The research aims to identify the causes and contributing factors to pedestrian crashes.
Dr. Jason Deller’s PhD thesis explores the complex relationship between roadway design and operating speeds, and how drivers respond to roadway design characteristics.
Dr. Hasti Tajtehranifard’s PhD thesis deals with the problem of traffic congestion caused by traffic incidents in metropolitan areas.
Dr. Basu’s PhD thesis investigates the influences of the built environment factors on walking route preference and safety. Her research studied the perceptions and preferences of pedestrian route choice in a typical suburban environment in Australia through a stated preference survey.
Dr. Trespalacios’s PhD thesis explores the mechanisms of compensatory behaviours in mobile phone distracted driving, which is a major cause of road accidents. He uses models of human behavioural adaptation and integrates driving behaviour models to develop a new model.
Dr. Mohammad Saifuzzaman’s PhD thesis aims to investigate the effect of distraction on car-following (CF) behaviour and proposes a new methodology to incorporate risk-taking and driver errors in CF modelling.
Dr. Rusli’s PhD thesis examined the characteristics of road traffic crashes on rural mountainous roads and compared these with the characteristics of crashes on roads located in non-mountainous areas.
Dr. Saeed’s PhD thesis was focused on understanding freeway traffic flow dynamics and their safety implication with a special focus on the role of human factors.
Dr. Arun’s PhD thesis introduces a new Road User Safety Field Theory to proactively assess traffic safety by studying the interactions of various road users at signalised intersections.
Dr. Amir’s PhD thesis reviews the theoretical assumptions underlying blackspot identification in transportation research, identifies gaps in the literature, and proposes a multiple risk source methodology for detecting motor vehicle crash blackspots.