About the Project
In an era of rapid technological changes, connected, electric and autonomous vehicles (CAEVs) seem to be the perfect solution for dealing with challenging problems such as congestion, pollution and space optimisation in urban areas. While significant efforts are put together for dealing with regulations, standard adoption and testing some trial cases, few studies have concentrated on studying the impact of adopting such novel technology and the problems coming along with operating in mixed traffic environments.
This PhD project aims at proposing both theoretical and transport modelling techniques in order to build an efficient traffic control mechanism responsible of balancing a shared and on-demand CAEV fleet across various urban areas and maintain a high level of service that would minimize travel time for all vehicles on the road (either autonomous, public transport, taxis, etc.). The underlying operational problem associated with the shared on-demand CAEVs is a sequential stochastic control problem with incoming dynamic requests for rideshare, routing optimisation and electrical recharge constraints.
In the first part of this project, the PhD student will develop an agent-based simulation tool (or a micro simulation model) for modelling the CAEV behaviour in a real urban setup and testing multiple assignment strategies -. This would include communication between vehicles and route optimisation for arriving in time at random recharge stations across the city subject to specific recharging constraints. Secondly, the focus will be on developing a controller supervision system that would allow traffic operators to balance the traffic demand and CAEV fleet allocation across large urban areas in order to ease traffic congestion . This would take into consideration a dynamic fleet reconfiguration which would operate under safe navigation conditions [4-5].
The PhD student will be located in the newly formed Future Mobility Lab at UTS cofounded by Dr. Mihaita, under a new Data Science Institute led by Prof. Fang Chen. The institute counts around 30 staff members with research interests spanning across asset management, transportation, behavioural data science and human dynamics. The Data Science Institute has both strong ties with industry, as well as world-class research, providing the ideal environment for solving real-world problems.
Interested candidates must have solid background knowledge in transport modelling, traffic control and mathematical modelling (preferably stochastic optimal control, dynamic routing), and strong programming capabilities in Python/R. Experience with transport modelling at micro and meso levels are a big plus. We are looking for a candidate with a master by research qualification and demonstrated research capabilities (publications). Candidates with publications in major conferences/journals will be prioritised.
The position will be open until the ideal candidate is identified.
In order to complete your application, please send us:
• your CV,
• grades transcripts from undergrad and Masters,
• your research proposal ideas on the topic (max. 2 pages; to be refined with supervisor and final selected candidate).
• Masters thesis manuscript (if applicable) or any other research thesis;
• a cover letter (no more than one page), outlining how your profile fits the PhD position;
• 3 referees (academic or industrial supervisors, co-authors): name, position and email;
• (if you have one) one of your publications which is most relevant for this position.
The selected PhD student will work under the supervision of Dr. Mihaita and interact closely with academics from the Future Transport Mobility Lab and a large industrial partner in Australia. Regular meetups and workshops will be organised for presenting new findings and results.
1. Mehdi N., Matthew J. R., Agent based model for dynamic ridesharing, Transportation Research Part C: Emerging Technologies, Volume 64, 2016, pp 117-132, ISSN 0968-090X.
2. Pereira, J.L.F, Rossetti, Rosaldo J.F. , An Integrated Architecture for Autonomous Vehicles Simulation, Proceedings of the 27th Annual ACM Symposium on Applied Computing, SAC 12, 2012, isbn 978-1-4503-0857-1.
3. Michael H., Hani S. Mahmassani, Dynamic autonomous vehicle fleet operations: Optimization-based strategies to assign AVs to immediate traveller demand requests, Transportation Research Part C: Emerging Technologies, Volume 92, 2018, pp 278-297.
4. Mao, T., Mihaita, A.S., Cai, C., Traffic Signal Control Optimisation under Severe Incident Conditions using Genetic Algorithm, ITS World Congress 2019, Singapore, 21-25 Oct 2019, Preprint: https://bit.ly/2ITBCwF
5. Mihaita A.S., Tyler P., Menon A., Wen T., Ou Y., Cai C., Chen F., "An investigation of positioning accuracy transmitted by connected heavy vehicles using DSRC", TRB 96th Annual Meeting, Washington D.C., 2017.