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Traffic Flow Prediction in Presence of Autonomous Vehicles with Consideration of Dynamic Traffic Assignment Model

   Department of Electrical and Electronic Engineering

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  Dr Shangbo Wang, Dr E G Lim, Prof A Marshall  Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

There has been an exponential increase in usage of autonomous vehicles across the globe in the past few years. Traffic flow prediction is important for autonomous vehicles using which they decide their itinerary and take adaptive decisions. Data-driven approaches such as deep learning models have been proved to be able to provide more accurate traffic flow prediction results compared to parametric methods like AutoRegressive Integrated Moving Average (ARIMA) models etc. However, data-driven models are black-box which normally are not interpretable and thus cannot reflect the exact insight of the causal relationship. In addition, most data-driven models only attempt to predict the future traffic state at the links deployed with sensors based on the corresponding historical data, prediction for unmeasured links is difficult to be tackled because of lack of measurements. To solve the problems, this project aims to propose a simulation-based traffic prediction approach by using Dynamic Traffic Assignment (DTA) models, which contains two major components that need to be learned from the actual historical data: OD (Origin-Destination) demand estimation and dynamic traffic assignment. The former is used to calibrate OD demand matrix from real data while the latter is used to assign each vehicle to the best route and determine the link traffic flow and route travel time by using the estimated OD demand matrix. The simulation-based traffic prediction can improve the prediction accuracy by capturing more realistic traffic flow characteristics such as shock waves, expansion waves, spillback etc. This project will also investigate the difference in DTA models with different penetration rate of autonomous vehicles.

For more information about doctoral scholarship and PhD programme at Xi’an Jiaotong-Liverpool University (XJTLU), please visit


The candidate should have a first class or upper second class honours degree, or a master’s degree (or equivalent qualification), in Transport Engineering, Computer Science, Electrical Engineering, Mathematics or a related field.

Evidence of good spoken and written English is essential. The candidate should have an IELTS score of 6.5 or above, if the first language is not English. This position is open to all qualified candidates irrespective of nationality.


The student will be awarded a PhD degree from the University of Liverpool (UK) upon successful completion of the program.

How to Apply:

Interested applicants are advised to email [Email Address Removed] (XJTLU principal supervisor’s email address) the following documents for initial review and assessment (please put the project title in the subject line).

  • CV
  • Two formal reference letters
  • Personal statement outlining your interest in the position
  • Certificates of English language qualifications (IELTS or equivalent)
  • Full academic transcripts in both Chinese and English (for international students, only the English version is required)
  • Verified certificates of education qualifications in both Chinese and English (for international students, only the English version is required) 
  • PDF copy of Master Degree dissertation (or an equivalent writing sample) and examiners reports available 

Funding Notes

The PhD studentship is available for three years subject to satisfactory progress by the student. The award covers tuition fees for three years (currently equivalent to RMB 80,000 per annum). It also provides up to RMB 16,500 to allow participation at international conferences during the period of the award. The scholarship holder is expected to carry out the major part of his or her research at XJTLU in Suzhou, China. However, he or she is eligible for a research study visit to the University of Liverpool up to six months, if this is required by the project.
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