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 need to understand the interactions of emerging mobility systems with traditional transportation modes, e.g., buses and privately-owned vehicles. To do so will require models that describe traffic dynamics and enable policymakers to explore a full range of solutions to transportation challenges. Solutions might entail fleet-size optimization, congestion pricing, or deployment of dwell zones to reduce empty miles, to name a few.

Traditionally, the vehicle trips on a network are modelled by tracking the flows of vehicles (with microscopic, mesoscopic, or macroscopic models) through individual links that compose the physical configuration of the network. After considerable data collection and computational efforts, results mostly apply to that specific network and setup. 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. There is a need to develop theories and parsimonious models that are able to capture the most important components of the system complexity so that the results and insights are robust and can be generalized.

This PhD project will be developed by taking another approach, which we refer to as the relative space, where one can treat the network as an undifferentiated unit and disregard the physical locations of numerous vehicle trips traversing the network. In this new relative space dimension, the trip flow dynamics are modelled based on the remaining trip distances to their respective destinations, see Vickrey (2020). Until recently, the relative space dimension has only been exploited through continuum models, such as the continuum bathtub models, which focus on the (aggregated) flow of initiated and completed trips. However, it is particularly 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. Therefore, the development of a multi-modal agent-based model in the relative space that is able to focus on individual trips has the potential to be an excellent method to study planning and management strategies for high-level policy questions in a computationally efficient manner.

During this PhD project, you will work within the hEAT lab (https://www.tudelft.nl/citg/heat-lab) on developing a multi-modal agent-based bathtub model that can handle trip chains for individuals across modes and regions. 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 network fundamental diagrams.
• Implement a the departure time choice model to the agent-based bathtub model.
• Develop a simulation model to capture trip chains of individuals efficiently. This model may include a multi-modal and/or a multi-region environment.
• Study first- and last-mile cooperation between shared autonomous vehicles and traditional transit systems.

As a successful applicant, you will have:

• A MSc degree in Transportation Engineering, Civil Engineering, Computer Science, Applied Mathematics, or closely related field.
• Fluent communication skills in English, both written and oral, as evidenced by a writing sample, such as a chapter from your Master’s Thesis or a (forthcoming or published) article or conference paper;
• Pro-active attitude, communication skills, and ability to work independently within a diverse team.
• Growth mindset, 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.

Fixed-term contract: 4 years.

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

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!

Faculty Civil Engineering & Geosciences

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.