Personalized mobility services and platforms such as shared bikes, ride-hailing, or on-demand public transit have gained substantial traction with the rise of digital communication technologies. Unlike traditional transportation modes, these dynamic mobility services offer several key advantages. Firstly, they provide more relevant travel advice, tailoring recommendations to individual interests and attitudes. Secondly, users have greater control over payment methods and subscription choices. However, personalization of mobility services requires certain amount of user data, behaviour, demographics, and travel history collected from users before it can be adapted to suit their demands. While extensive collection of data enables customization, it also raises privacy concerns and prompts questions about potential data misuse. Striking a balance between consumer trust and the quality of personalized mobility services is highly relevant and urgent in the era of artificial intelligence, social media, and disinformation.

The current research aims to understand citizens willingness to share different types of personal data in exchange of personal and collective benefits.  Personal benefits such as travel time savings, avoiding crowds in transportation stations as well as activity locations, travel discounts (dynamic pricing), or route recommendations. Collective benefits include contribution to sustainable travel, lower emissions or energy consumption. Moreover, an agent-based simulation will be conducted in which the difference in saving time and cost (individually and collectively) in a multimodal transportation system, as a result of various number of citizens who share various amount and type of data, will be calculated. A Dutch city and a (hypothetical) MaaS platform will be the basis of the simulation.

The Urban Planning and Transportation group in the faculty of the Built Environment is looking for a highly motivated and excellent PhD candidate interested in the area of travel behaviour modelling research. The PhD research direction will include topics such as, but not restricted to:

• Psychology and behavioural theory of travel choice demand.
• Discrete choice modelling, integrated modelling, and machine learning methods for travel behavior analysis.
• Agent based simulation.

If you're passionate about advancing the state-of-the-art on mobility solutions with AI, and contributing to impactful research on responsible mobility, we invite you to apply for this exciting PhD position.

• A master's degree (or an equivalent university degree) in data science with minor in transportation, or travel behavior, transportation engineering with rich knowledge in behavioral modeling, deep learning and programming, operational research, or related fields.
• A research-oriented attitude.
• Ability to work in an interdisciplinary team and interest in collaborating with industrial partners.
• Motivated to develop your teaching skills and coaching students.
• Fluent in spoken and written English (C1 level or higher).

Any of the following could be considered an advantage and should be mentioned in the motivation letter:

• Experience in software development (Python, R, C++), or deep learning libraries (PyTorch, JAX, etc.)
• Previous experience with national or international data-driven policy making.
• Previous publications and/or scientific communications.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.