Future,
AND INTELLIGENT TRANSPORT SYSTEMS
This theme utilizes advanced data analytics and computer simulations to create innovative transport modelling solutions. We aim to understand travel behavior and patterns better to inform the planning of future intelligent transport networks. By leveraging big data and machine learning, we explore ways to predict demand, simulate passenger flows, and optimize system operations across transport modes. Our focus areas include connected and automated vehicles, mobility-as-a-service, smart infrastructure, and technologies for seamless multimodal travel.
In particular, we focus on:
- Intelligent Transport Systems (ITS)
- Artificial Intelligence
- Big Data Analytics
- Electric vehicles
- Autonomous vehicles
- Connected vehicles (V2V, V2I, V2X)
- Traffic operations and optimisation
- Transport planning and modelling
- Future Transport Funding
- Travel Demand Management
- Congestion, time and location use charging
MINH KIEU
Theme Leader
Senior Lecturer Civil and Environmental Engineering
Towards real-time predictions using emulators of agent-based models
A new research showcases a breakthrough in real-time predictions of complex systems, like pedestrian movements, through advanced emulation techniques that overcome the limitations of Agent-Based Models. Minh Le Kieu, Hoang Nguyen, Jonathan A. Ward, Nick Malleson...
Perception, experience and resilience to risks: a global analysis
A groundbreaking study unveils a comprehensive global analysis of individual risk perceptions and resilience, revealing significant disparities and the pivotal role of income perception in shaping resilience.. Minh Kieu & Gayani Senanayake Please read the...
A class-specific soft voting framework for customer booking prediction in on-demand transport
A new research introduces a groundbreaking ensemble learning method to precisely predict customer bookings for suburban and rural On-Demand Transport services, overcoming data scarcity challenges Le-Minh Kieu, Yuming Ou, Long T. Truong, Chen Cai Please read the...
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