In recent decades, the transportation sector, like many others, has witnessed cycles of 'hype and disappointment', particularly with recent innovations like Mobility as a Service (MaaS) and other platform-based mobility services. Despite the transformative potential of these new mobility solutions, adoption rates often fall short of expectations. This gap between promise and reality highlights the need for a deeper understanding of the complex decision-making processes that individuals and households face when considering new mobility solutions.

Existing research has investigated factors influencing the adoption of new mobility services, often focusing on aspects such as service attributes, socio-demographic characteristics, attitudes, and the built environment. While these studies offer useful insights, they often view decision-making as a static snapshot, focusing on choices made at a single point in time while overlooking the dynamic nature of the process.  In reality, adopting new mobility services like MaaS involves a dynamic series of decisions over time, marked by significant uncertainty. Unlike minor adjustments such as switching to streaming media platforms, adopting MaaS requires breaking long-standing travel habits and forming new ones. This uncertainty extends beyond initial adoption to include concerns about service reliability and availability. For example, users might worry about the availability of shared transportation modes included in their MaaS subscription, which can impact their overall satisfaction and willingness to continue using the service. These uncertainties raise critical questions about the viability of MaaS in daily life: Will users fully utilize their subscription benefits? How reliable are the transportation options provided through the MaaS service? Will MaaS prove to be a reliable alternative to private car ownership? Addressing these concerns is essential for ensuring that MaaS not only attracts users but also retains them, ultimately promoting its widespread adoption and diffusion.

This PhD research aims to capture the complexity of the adoption process, rather than simplifying it. By focusing on the temporal progression of decision-making and evolutionary elements like uncertainty, we seek to develop models that provide a "moving picture" of adoption behaviour, ultimately offering insights that can help bridge the gap between technological promise and actual adoption. This approach may involve employing advanced theoretical models, including regret-based decision-making frameworks, and innovative data collection methods, such as serious games, integrated with agent-based models to track behaviour over time.

The Urban Planning and Transportation group in the department of the Built Environment is looking for a highly motivated and skilled PhD candidate to work in the area of innovation adoption and behavioural modelling in transportation, a key area in developing sustainable transportation systems. The PhD research direction will include topics such as, but not limited to:

• Psychological and behavioural theories related to the decision-making process for adopting emerging mobility solutions.
• Advanced choice modeling to address uncertainties in choice behaviour (e.g. regret-based models) and agent-based modelling to capture the dynamic evolution of decision-making.
• Data collection methods (e.g. serious game, stated preference surveys) to understand evolving user behaviors.

If you're passionate about advancing the understanding of innovation adoption in transportation and contributing to cutting-edge research on emerging mobility services like MaaS, we invite you to apply for this exciting PhD opportunity.

• A master's degree (or an equivalent university degree) in transportation engineering, civil and environmental engineering, behavioural economics, or a comparable domain.
• Possess quantitative methodological skills such as advanced statistical analysis and experience with behavioural modelling.
• 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 coach 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 with statistical and programming tools (e.g., Python, R, SPSS, JavaScript).
• Previous experience with simulation models (e.g. agent-based models).
• 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. €2,872 max. €3,670).
• 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.