Chintan Advani’s PhD topic is ‘Empirical modelling of traffic states and route choice behaviour’. This profile outlines his work, lessons learnt, and more.
Download the three final reports from iMOVE’s ‘Improved network performance prediction through data-driven analytics and simulation’ project.
This project aims to improve travel demand calibration and accuracy of 24 hour/ 7 days network simulation models for any hour of any day.
In this PhD project data analytics on Bluetooth trajectories and traffic states will be applied to empirically estimate the assignment matrix for the network.
This project will offer a real-time decision support tool for traffic operations centres to predict network congestion, and evaluate the possible responses.
This project will see an Aimsun Live pilot system installed in Queensland, providing real-time simulation-based prediction, projecting 60 minutes ahead.
iMOVE now has 50 projects either on the go or completed. Find out more about the latest 10, who’s leading them, and where they are taking place.
R&D of new algorithms to process raw data generated by the current RT4 radar, assigning an Austroads classification to all detected vehicles.
This PhD looks at prediction of traffic disruptions in cities using AI and synergising traffic simulation modelling to help traffic authorities respond.
Akshay Vij develops statistical models that can be used to understand and predict human behaviour, particularly in transport about him and his work.
This project aims to improve the ability of road authorities to predict network performance in the short-term using data-driven analytics and to estimate the impact of automated vehicles (AVs) in longer-term predictions.
Western Australia kicks off its iMOVE involvement with two projects, one looking at traffic prediction and preparation for connected vehicles, the other planning freight and trade logistics strategy for the next 50-100 years for Perth and its surrounds.