As the world increases its population, getting from place to place becomes more challenging if we do not find new ways to do move around.
This theme will focus on how to improve mobility for all whilst working towards zero emissions and minimal impact on the environment. It will also address how we can meet the future demand.
There are so many questions to answer and attitudes to change such as shifting mindsets from ownership to sharing.
There are transport systems in place, so how can these improve and be better integrated? How can the technologies used and data collected by Traffic Management Systems be better managed?
Finally, what are the gaps in knowledge and existing infrastructure/ operating systems that prevent us from making progress.
Research
We are eager to work with people and organisations on research that fits this theme. Some possible research examples might be:
- how to optimise the return on Advance Traffic Management Systems (ATMS)?
- to what extent can people flow management through image processing technology help predict and manage congestion?
- what technologies are most suitable to protect vulnerable road users from car and truck accidents and what is the path to mass deployment of these technologies?
If you have an idea for research and want to be a part of our network, contact us with your ideas.
Engagement
We are able to undertake work for organisations and governments. Talk to us about how we can help you. Some examples include:
- analysis of data and provision of reports to advise on planning public transport in fast growing cities
- feasibility studies – for example, strategies to win more passengers for shared modes of transport
- economic modelling – for example, what are the cost benefits of smart mobility for improved public transport linkages, walkability, multilevel parking and improved design of roads
Current research projects
Real-time crash prediction in disordered traffic
This project aims to develop real-time crash prediction models for disordered traffic on urban roads and intersections.
Utilising high resolution multimodal trajectory data being collected by an on-going project at IIT Kanpur, surrogate safety measure and crash precursors will be identified and analysed.
Real-time crash prediction models with different predictive horizons will then be developed using deep learning techniques such as short-term memory networks.
Investigators:
Project ID: 2-EOI-IITK
Electric-assist bicycle (e-Bike) mobility planning for smart cities: Fostering active transportation to improve public health, air-quality, sustainability, and efficiency during COVID-19
This project will investigate whether e-bikes offer a better replacement to motor vehicles than unassisted bicycles and thus encourage people to use this clean and healthy mode of transport to improve quality of life through reduced traffic congestion and carbon emissions. It also will investigate whether it will assist those with a mostly sedentary lifestyle to increase their levels of physical activity and reduce the risk of health challenges.
Investigators:
PhD candidate:
Project ID: 10-EOI-BITSP
Future of India-Australia Agribusiness Trade in an Era of Reglobalization: A Supply Chain Management Perspective
This research aims to explore how supply chain collaboration among groups of importers and/or exporters can successfully reduce production/logistical costs along with work for new trade agreements. We will combine national input output data along with trade statistics to identify the global value chains in various sectors and decipher value added in bilateral trade flows. The study will generate innovative supply chain solutions and promote collaboration among multiple clusters of Indian and Australian exporters and importers, with special focus on micro, small and medium enterprises and low-income consumers.
Investigators:
Project ID: 47-EOI-IITK
Growth Convergence & Spatial Analysis of Indo‐Australian Supply Chain Dynamics Perspective
Investigators:
Project ID: EOI 2-1-IITK
Automated traffic incident detection and duration prediction
Investigators:
Project ID: EOI 2-7-IITK
Incremental data stream representation learning based digital twin technology for dynamic smart city environments
Investigators:
PhD candidate:
- Mr Shrey Verma
- Ms Namita Kumari
Project ID: 150-IIT K - LTU 2021
Hybrid Nanocellulose‐Glass and Carbon Fibres: Toward Sustainable Sourcing and Biodegradable Recycling of High‐Performance Structural Composite Materials
Investigators:
Project ID: EOI 2-13-IITK
METACOMP: Metamaterial embedded composites for enhanced multifunctional mechanical performances
Investigators:
Project ID: EOI 2-14-IITK
Integrating supply chains in smart cities for enhanced community service: A business and environmental sustainability approach
Investigators:
Project ID: 101 BITS - LTU 2023
Hybrid Energy Storage System (Batteries + Supercapacitors) for developing Next Generation Electric Vehicles
Investigators:
Project ID: 51 BITS - LTU 2023
Resource Allocation for Low-power networks in Smart Cities
Investigators:
Project ID: 24 BITS - LTU 2023
Safety zoning of Highways using Satellite Imaging & Deep Learning
Investigators:
Project ID: 119 BITS - LTU 2023
Research partners
La Trobe Academics
You can download the complete file of one-page profiles for La Trobe Academics here [PDF 1.3MB]
Indian Institute of Technology (IIT) Kanpur Academics
You can download the complete file of one-page profiles for IIT Kanpur Academics here [PDF 1.1MB]
Birla Institute of Technology and Science (BITS) Pilani Academics
You can download the complete file of one page profiles for BITS Pilani Academics here [PDF 1.3MB]
