How to encourage participation in social routing schemes? (Master thesis)
Urban road congestion remains a major challenge, and expanding infrastructure is often costly and impractical. Social routing offers an alternative, where commuters voluntarily take longer ‘social’ routes on some trips each week to improve overall traffic efficiency.
A simulation framework for Lyon, France, has assessed the long-term impact of such schemes, integrating dynamic traffic assignment and behavioral choice models. Findings suggest that some users opt out due to journey-specific factors and personal attitudes toward altruism.
This project will explore strategies to encourage participation among reluctant users. Key questions include:
• Adaptive Participation Offers – Can varying the number of days a user is asked to take the longer route make participation more attractive?
• Temporary Incentives & Nudging – Can short-term offers encourage enough participation to shift the network toward a better operational state, even after the incentives are removed?
Contact: Alexander Roocroft (a.roocroft@tudelft.nl); Marco Rinaldi (m.rindali@tudelft.nl)
Drive-By Sensing for Urban Monitoring: An Optimization Framework (Master thesis)
Drive-by sensing is a relatively new approach that leverages mobile platforms such as buses, taxis, and bikes to provide extensive spatiotemporal coverage of sensor networks for various applications, including air pollution monitoring, traffic monitoring, and pedestrian flow monitoring.
This thesis aims to develop an optimization model for sensor networks by integrating mobile platforms with the objective of maximizing spatiotemporal sensor network coverage while minimizing costs. The proposed model determines the optimal combination of mobile platforms for sensing tasks based on schedules and real trajectories. The decision variables include the type of sensors, as well as the type and number of mobile platforms used for sensing tasks. This approach will be applied to a real-world case study in the Netherlands, focusing on a specific application to enhance sensor network performance while considering constraints such as budget and accuracy.
The project will involve the following steps:
• Collect and process data on the trajectories of mobile platforms.
• Develop an optimization model for a sensor network by leveraging mobile platforms for sensing tasks.
• Apply the proposed approach to a real-world case study to evaluate the performance of the developed model.
Contact: Mohammad Jafari (M.Jafari@tudelft.nl); Maaike Snelder (M.Snelder@tudelft.nl)
Rotterdam city centre: If Hofplein is car-low, where do the cars go? (Master thesis)
In numerous city centers across Europe and beyond, traditionally car-oriented environments are undergoing transformative redevelopment into people-centric spaces. These transitions aim to improve urban livability, enhance environmental quality and increase active/shared mobility.
However, there is limited empirical evidence on the actual behavioral responses these redesigns elicit. Specifically, it remains unclear whether observed changes in travel patterns are primarily the result of existing users adopting more sustainable travel behaviors, or whether these patterns are driven by different user groups altogether—possibly displacing car traffic to other areas rather than reducing it.
This distinction is crucial for evaluating the real-world effectiveness and equity of such interventions in promoting sustainable urban mobility.
This MSc thesis project will focus on the redevelopment of the iconic Hofplein in Rotterdam.
Contact: Prof Dr Ir Bart van Arem (b.vanarem@tudelft.nl); Ir Sander Buningh BAM Infra (sander.buningh@bam.com); Dr Maaike Snelder, (m.Snelder@tudelft.nl).
Public Acceptance and Behavioral Impacts of Individual and Combined Low-Car Interventions in the Netherlands: A eXtended Reality Study (Master Thesis)
Transitioning to a less car-oriented urban mobility system is a crucial yet challenging task for cities worldwide. Interventions like improving public transport, expanding cycling infrastructure, and implementing car-free zones aim to reduce private vehicle use and promote sustainable mobility. However, these measures often face public resistance, with acceptance shaped by perceptions of inconvenience and limited understanding of their benefits. Understanding how residents perceive and adapt to low-car strategies is thus essential for aligning urban planning with public expectations and ensuring long-term adoption.
This thesis seeks to address this gap by investigating people’s acceptance of various low-car interventions, the underlying reasons behind their perceptions, and how these measures affect their travel behaviours when implemented. A stated preference survey combined with eXtended Reality (XR) technology will be the primary method to capture responses. Compared to traditional surveys, XR offers immersive, interactive experiences, allowing participants to engage with realistic scenarios. This leads to more accurate, contextually rich data by simulating real-world environments, enhancing the validity of responses.
This research aims to inform the development of inclusive and effective low-car strategies, contributing to the broader goal of sustainable urban mobility. Additionally, it seeks to explore how to combine interventions to maximise public acceptance and ensure cohesive, well-received implementation plans.
Large Language Model for Individual Travel Behavior Generation in the Netherlands (With Internship at TNO) (Master thesis)
Urban mobility planning heavily relies on understanding individual activity patterns to design sustainable and efficient transportation systems. Traditional methods for generating synthetic travel demand often struggle with the lack of data and privacy constraintsmaking the generation process time-consuming and challenging to transfer to different regions. To overcome these challenges, this study explores the integration of large language models (LLMs) into the process of synthetic travel demand generation. LLMs offer a promising approach by leveraging their ability to process semantically rich data and align it with contextual information.
However, questions still remain for this topic, including: What are the suitable data inputs for large language models in synthetic travel demand generation? Which large language model is most appropriate for this application? How can the generated outputs be validated against real-world scenarios?
