With increasing population growth and expanding urban sprawl, transport and infrastructure managers will require smart integrated AI assistive technologies and decision support tools.
Let the data talk
These tools harness the power of big data and artificial intelligence for predictive maintenances, disaster management strategies, as well as optimising multimodal passenger and heavy haul systems to ensure optimal socioeconomic outcomes. This will be critical to managing infrastructure assets more effectively over larger geographic areas with limited resources and competing priorities.
This program aims to develop next-generation smart transportation technology and tools by integrating big data analytics and hyper-automation with conventional civil engineering discipline, to enable transport asset managers and practical engineers to establish and optimize their predictive maintenance plan to make better data-driven decisions.
Specific research areas
- Big data analytics for asset management (e.g. railway track, road, bridge) to provide insights for infrastructure and asset inspections
- Develop track/road predictive maintenance models based on big-data analysis to monitor and predict the probability and types of instability mechanisms in terms of space and time
- Data analytics for incident management to capture the crucial data to enhance the initiatives and proactively manage the incidents
- Predictive/data analytics to optimise network performance (e.g. train performance prediction)
- Integrate data analytics with traffic simulation.
- The outcomes of these research areas will support optimising passenger mobility, efficient freight movement and advanced infrastructure networks, whilst minimising transport infrastructure maintenance and upgrading costs.