"PhD Position Behavioral Models Towards the Design of Mixed Fixed-Flexible Transport Networks"

The increase of public transport usage has clear potential in transforming our environment to be more liveable, sustainable and convenient. However, to ensure economic viability with off-peak times and relatively remote locations, while increasing the attractiveness to the users, we need innovative designs where fixed and flexible services support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies.

In FlexMobility we propose a holistic approach to design a public transport network that includes both traditional fixed lines and flexible on-demand services, while considering the underlying travel behaviour. To reach a user-centric public transport system we will also study how the demand would react to such a mixed system. We will do so by combining revealed preferences data on the already available systems together with stated preferences in tailored surveys for enriching the understanding of the mixed system. The behavioural understanding on the demand side will provide information to the supply side, such as the trade-offs between different attributes of the system, or the acceptable walking and waiting times, which will be used both to adapt the design and to have more realistic simulations. Supply and demand levels are intimately intertwined. While the chosen design has a significant effect on the most important aspects of users’ experience, the algorithms,and thus the supply decisions, are based on users’ preferences.

This PhD position within FlexMobility, will focus on the supply-demand interactions to guide the design of mixed fixed-flexible transport networks. This will start with the behavioral investigation based on the available data as well as the new survey data to be collected. The purpose of this first step will be to identify the key attributes of the transport system that influence the preferences of the users while accounting for the behavioral heterogeneity in the population. Methodologically, we might explore both random utility models as well as machine learning, potentially in a hybrid way, in order to capture complex phenomenon. The behavioral understanding will guide the representation of supply-demand interactions in the next step. A baseline methodology will be an iterative process between supply and demand models, which will then be expanded towards endogenous representation of behavioral models in the network design problem. This integration of supply-demand interaction will build on optimization models of the transport network and will be enhanced with the behavioral representations. The decisions to be optimized could potentially exploit model-based learning approaches.

In this work, we will consider two use cases: (1) bus network considering both fixed-line buses and on-demand ones (2) a network with water-taxis which operate in fixed and flexible modes at different times of the day. For both use cases, there will be interaction with the project partners for generating/obtaining the needed data as well as for setting up realistic case studies.

The position is available as of October 1, 2025 with a flexible start date to be agreed upon. The PhD candidate will join the group of Bilge Atasoy, with various researchers working on adaptive transport and logistics systems through methodologies of dynamic and predictive optimization, behavioral modeling and machine learning. There is vivid interaction within the group to foster collaboration both with scientific and social activities. The PhD candidate will also interact with the group of Javier Alonso-Mora as part of the FlexMobility project as there will be in total 2 PhDs and 1 postdoctoral researcher working in a collaborative way to reach the goal of designing user-centric flexible transport networks. As part of the PhD position, there will be opportunities to gain teaching experience in the relevant courses and/or supervising MSc students.

We are looking for a candidate with transportation research knowledge who is comfortable with operations research methodologies as well as behavioral modeling. As the project is a multi-faceted one, we expect candidates with an appreciation of the interaction between behavioral modeling, operations research and machine learning.

Master of Science (MSc) diploma in Transportation, Logistics, Operations Research, Industrial Engineering, Applied Maths or any other related field. Attitude to function both in a team and independently. Willingness to conduct multidisciplinary research in collaboration with both scientific and industrial partners. Drive for excellence in research. TOEFL or IELTS English proficiency tests for all applicants except those graduated from an MSc program that was taught in English. The minimum requirement of a TOEFL score of 100 IELTS of 7.0 per sub-skill (writing, reading, listening, speaking).

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 € 2,770 gross per month in the first year to € 3,539 gross per month in the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%.