About Chintan’s research
His research focuses on the empirical modelling of the route choice behaviour for real urban networks by leveraging the large-scale vehicle trajectory dataset.
Route choice modelling is an essential input for most infrastructure planning, transport policymaking and traffic management strategies. Major research is done in this domain, but with a limited dataset. The availability of large-scale real trajectories provides a unique opportunity to empirically capture the route choice behaviour.
His research is of interest to the Department of Transport and Main Roads (Queensland) and Aimsun, and he’s receiving valuable feedback from these industry partners.
What questions will this research answer?
Chintan is focusing on two key issues:
- How do we generate the trajectory and identify the observed routes using the Bluetooth datasets?
- How can we systematically capture route choice behaviour using a noisy trajectory dataset?
Video presentation of Chintan’s work
Reflections on his PhD
Initially, engaging and managing multiple activities was a major difficulty. If he could travel back in time, Chintan would advise himself to be more patient, as he underestimated the length of time to complete some tasks.
Other improvements along the journey have been enhancing his domain skills such as programming, visualisation tools, simulation, and data analysis software. Further, supervisors have helped Chintan immensely with his soft skills, including report / technical writing, storytelling, communicating to a larger audience, and resource management.
Following his PhD, Chintan would like to work on publishing his research and work alongside the practitioners to implement it in the real world. Further, he would like to use his research and entrepreneur skills to start his own business and solve some essential challenges in transportation.
A word from PhD supervisor, Ashish Bhaskar
Why is this PhD important to investigate?
This PhD research develops novel techniques to mine traffic data (vehicle trajectory, traffic congestion) to better model the route selection of travellers on our road network. The modelling is used to better calibrate traffic assignment models used in traffic simulation.
What are the major challenges to overcome in the field?
The research is highly applied and focuses on real traffic data which is very noisy and heterogeneous. Advanced models are developed to account for the accuracy and reliability of the real data. The developed model should be scalable to account for the complexity with the large-scale network.
Where might this work lead in the (near and far) future?
Empirical estimation of the traffic assignment – the consideration of which in-traffic simulation should significantly reduce the computational cost of the real-time simulation of the large urban networks (such as Aimsun Live).
If you’d like to contact Chintan about his research, please click the button below.