PHD Developing Efficient Agent-Based Model for Multi-Region and Multi-Modal Transport

Are you interested in studying multi-modal transport systems and proposing cooperative management strategies for a more efficient and sustainable mobility? Through this PhD, you will help develop an agent-based model built on aggregated traffic flow models to establish universal insights on multi-modal transport management strategies!

It is well-agreed by experts that it is no longer sustainable to have transport systems relying primarily on privately-owned vehicles to serve the travel demand. Fortunately, emerging technologies have allowed the development of new solutions and services for urban travel. These emerging mobility systems, such as on-demand for-hire vehicles (Uber, Lyft, etc.) and connected and automated vehicles, are becoming part of the urban landscape. To develop policies and management strategies to serve society best, we must understand the interactions of emerging mobility systems with traditional transportation modes, e.g., transit systems and privately-owned vehicles. To do so, we need models that can capture the interaction between transportation modes, their impact on traffic dynamics (travel speed, congestion patterns, etc.), and the impact of traffic dynamics on the mode and departure time choice. Such models will enable policymakers to explore various solutions to transportation challenges. Most current simulation models incorporating multi-modal transport require a lot of data collection and computational costs to simulate a specific network system. Consequently, insights and conclusions are often site-specific and, therefore, can rarely be extended to other regions when studying transport management strategies for high-level policy questions.

In recent years, there has been increased attention to very aggerated models that can capture the traffic dynamics in an urban zone as a queueing system. These models are referred to as bathtub or reservoir models and are built on several assumptions, see foreword in Vickrey  (2020). The speed of the vehicles in that zone is determined by the so-called macroscopic or network fundamental diagram, see Johari (2021). With this model, one can treat the network as an undifferentiated unit and disregard the physical locations of numerous vehicle trips traversing the network. Such models are simple and can be solved numerically in a very efficient computational way. Although most literature studies consider continuum bathtub models, one can also take the trip perspective. This is very important to be able to study individuals' experiences and decisions when designing management strategies at the network level (e.g., pricing strategies) to improve the overall transportation system. This PhD project will be focused on developing an agent-based simulation model for multi-modal transport systems based on the assumptions of the bathtub models. The agent-based bathtub model will capture trip chains for individuals across modes and regions and will be used to design planning and management strategies for high-level policy questions.

During this PhD project, you will work within the hEAT lab (https://www.tudelft.nl/citg/heat-lab ). Some main elements of the project you will work on include the following:

• Review the existing state of the art on bathtub models, trip-based and agent-based modelling, and multi-region and multi-modal network fundamental diagrams.
• Incorporate the departure time choice model into the agent-based bathtub model.
• Develop a agent-based bathtub simulation to capture trip chains of individuals efficiently. This model may include a multi-modal and/or a multi-region environment.
• Design a cooperative strategy between emerging mobility and traditional transit systems.

As a successful applicant, you will have:

• A MSc degree in Transportation Engineering, Civil Engineering, Computer Science, Applied Mathematics, Mechanical Engineering, or closely related field;
• Fluent communication skills in English, both written and oral;
• Growth mindset, pro-active attitude, curiosity, and perseverance;
• Strong programming skills are a plus. For example, in MATLAB, Python, or C++;
• Knowledge of control theory is a plus.

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements .

If your mother language is not English and you do not hold a degree from an institution in which English is the language of instruction, you must submit proof of English proficiency from either TOEFL (minimum total score of 100) or IELTS (minimum total score of 7.0). Proof of English language proficiency certificates older than two years is not accepted.

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2541 per month in the first year to € 3247 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes and covers societal challenges such as climate change, energy transition, resource depletion, urbanisation and the availability of clean water, conducted  in close cooperation with a wide range of research institutions. CEG is convinced that Open Science helps to achieve our goals and supports its scientists in integrating Open Science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic Engineering and Water Management.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.

For more information about this vacancy, please contact Dr. Ir. Irene Martinez, e-mail: [email protected].

Are you interested in this vacancy? Please apply no later than 11 April 2023 via the application button. Please include in your application:

• A motivation letter (max. 1 page) covering your research interest and relevant experience in relation to the position;
• 1-2 page CV including publication list (if applicable);
• One-page summary of your MSc thesis;
• A writing sample, such as a chapter from your Master’s Thesis or a (forthcoming or published) article or presented conference paper;
• Transcripts of academic qualifications (BSc and MSc) including list of courses and marks;
• Names and contact information of at least two references.

We expect to hold the job interviews in May 2023.

Please note:

• You should apply online. We will not process applications sent by email and/or post.
• A pre-employment screening can be part of the selection procedure.
• Acquisition in response to this vacancy is not appreciated.
• You may be asked to complete a coding assignment as part of the selection process.