PhD scholarship in Dynamic Traffic Assignment with Integrated Behavioural Modelling

mandag 08 jul 19

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Frist 31. august 2019
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DTU Management at the Technical University of Denmark invites applications for a 3-year PhD position in the Network modelling group of the division of Transport. The topic of the project concerns developing Dynamic Traffic Assignment models that embed realistic and consistent behavioural modelling.

Transport networks have been shown to highly influence the way people organise their lives. In order to optimise the daily operation and to maximise the benefits of potential infrastructure investments (e.g., road pricing, new roads), it is thus crucial that the decision process is supported by a well-functioning traffic assignment model. This model should give a realistic representation of the route choices of travellers as well as the network conditions. 

Models for traffic assignment that embed behaviourally realistic route choice have been developed and applied. These have however mainly been static models. Such models fail to capture the temporal dimension of congestion, thus hindering accounting for the effects of such variations in the route choice and the overall results. Applying static models in urban areas with evident congestion introduces bias and leads to unrealistic predictions of network conditions and effects.

On the other hand, disaggregate models for dynamic traffic assignment (DTA) have also been developed. Opposed to the static models, these manages to capture the temporal variations of travel demand. This provides better predictions of travel times and network conditions in urban areas. However, all such models are based on overly simplified route choice criteria and often simulation. This leads to inconsistencies, behaviourally unrealistic route choices and unrealistic predictions of network conditions.

In this project you will work closely with the supervisory team on bridging the gap between the two research directions.

Responsibilities and tasks
The aim of the project is to implement state-of-the-art behaviourally realistic route choice models into DTA models. Only thereby is it possible to analyse correctly the impacts of infrastructure investments (e.g., introduction of road pricing or construction of new infrastructure) or optimize daily operation (e.g., provide traveller information or adapt signal plans). Developing such a model is however not trivial, but if done consistently it will induce realistic predictions of the temporal dynamics of route choice, network travel times and flows. You will work closely with the supervisory team on tackling this challenge.

During the project, we will give methodological contributions by formulating a novel consistent model framework that embeds realistic behavioural models based on e.g. Random Utility Theory into disaggregate theoretically well-explained models for dynamic traffic assignment in congested areas. We will also give practical and empirical contributions by developing and implementing corresponding efficient solution algorithms and applying these for large-scale real-life cases. Consequently, some of the tasks to be undertaken by you and the supervisory team include:

  • Perform literature review on behaviourally realistic route choice models as well as current trends within DTA models
  • Design a theoretical model that facilitates behaviourally realistic modelling of route choice within a DTA modelling framework
  • Develop and implement corresponding solution methods
  • Test and evaluate the methods by application to small-scale as well as real-life large-scale cases
  • Prepare manuscripts for journal publication
This project is a partnership with the Israel Institute of Technology (Technion), and the student is expected to do an extended research visit to Technion.

Candidates should have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. The degree should be within Transportation Modelling, Applied Mathematics and Statistics, Data Science, Behavioural Studies, or related; 
  • Excellent background in statistics and probabilities is required
  • Transportation modelling disciplines in the education background are preferable
  • Excellent programming capabilities are preferable, e.g. in Python, Matlab or Java
The following soft skills are also important:   
  • Good communication skills in English, both written and orally
  • Willingness to engage in group-work with a multi-national team
Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see the DTU PhD Guide

The assessment of the applicants will be made by 1 October 2019.

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years. 

You can read more about 
career paths at DTU here.

Further information
Further information may be obtained from Professor Otto Anker Nielsen,, or Associate Professor Thomas Kjær Rasmussen,   

You can read more about DTU Management at

Please submit your online application no later than 31 August 2019 (local time)Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply online", fill out the online application form, and attach all your materials in English in one PDF file. The file must include: 
  • A letter motivating the application (cover letter)
  • Curriculum vitae
  • Grade transcripts and BSc/MSc diploma
  • Excel sheet with translation of grades to the Danish grading system (see guidelines and Excel spreadsheet here)
Candidates may apply prior to obtaining their master's degree but cannot begin before having received it.

Applications and enclosures received after the deadline will not be considered.

All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.

The Network Modelling group belongs to the Transport division of the Department of Technology, Management and Economics (DTU Management) at DTU. The division conducts research and teaching in the field of traffic and transport planning and appraisal, with particular focus on behaviour modelling, network modelling, machine learning and simulation, transport economics and safety.  

DTU Management Engineering conducts high-level research and teaching with a focus on sustainability, transport, innovation and management science. Our goal is to create knowledge on the societal aspects of technology - including the interaction between technology and sustainability, business growth, infrastructure and prosperity. Therefore, we explore and create value in the areas of management science, innovation and design thinking, business analytics, systems and risk analyses, human behaviour, regulation and policy analysis. The department offers teaching from introductionary to advanced courses/projects at BSc, MSc and PhD level. The Department has a staff of app. 350. Read more at  

DTU is a technical university providing internationally leading research, education, innovation and scientific advice. Our staff of 6,000 advance science and technology to create innovative solutions that meet the demands of society, and our 11,200 students are being educated to address the technological challenges of the future. DTU is an independent university collaborating globally with business, industry, government and public agencies.