Proactive micromobility safety assessment using AI-based video analytics and traffic conflict techniques

Overview

A fully funded PhD scholarship is available in the School of Civil and Environmental Engineering at Queensland University of Technology (QUT) as part of a newly awarded Australian Research Council (ARC) Discovery Project titled 'Shaping net-zero cities with safe and efficient micromobility solutions'.

This PhD project will investigate the behavioural and safety interactions between pedestrians, micromobility users (e.g. e-scooters and e-bikes), and other road users in shared urban environments. The research will combine AI-based video analytics, trajectory analysis, behavioural modelling, and advanced statistical safety methods to develop next-generation proactive safety assessment frameworks for multimodal transport systems.

Research activities

The PhD will focus on the development of behaviourally informed safety models capable of capturing complex interactions between vulnerable road users in shared spaces and mixed-traffic environments.

The research will involve:

• AI-based video analytics and computer vision techniques for extracting trajectories and interaction events from real-world traffic footage
• analysis of pedestrian–micromobility and micromobility–vehicle interactions in shared urban environments
• traffic conflict analysis using trajectory-level interactions
• application of advanced statistical techniques and machine learning models for proactive crash risk estimation
• integration of behavioural safety models into broader multimodal transport modelling frameworks.

Outcomes

The project is expected to generate new theoretical and practical insights into the behavioural and safety implications of micromobility within complex urban transport systems. It will advance understanding of how micromobility interacts with other road users in dynamic environments and contribute to the development of proactive, data-driven safety assessment frameworks. These frameworks will support evidence-based decision-making and inform the design and management of safer, more efficient, and more sustainable multimodal mobility systems.

Skills and experience

• A master degree (or equivalent) in:
  • civil engineering
  • transport engineering
  • a closely related discipline.
• Strong analytical, quantitative, and problem-solving skills.
• Experience in:
  • statistical modelling
  • machine learning
  • econometrics
  • other mathematical modelling approaches.
• Programming experience in Python, R, or similar programming environments.
• Strong technical writing and communication skills, with the ability to present complex ideas clearly and effectively.

Scholarships

You may be eligible to apply for a research scholarship.

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Keywords

• transport safety
• traffic conflict technique
• e-scooter
• shared space
• econometric model
• multimodal transport system

Contact

To apply for this position, submit the following documents via email m1.haque@qut.edu.au:

• a detailed curriculum vitae (CV) highlighting academic achievements, technical skills, research experience, and publications
• academic transcripts from previous degrees
• a cover letter outlining your research interests, motivation for the position, and relevant experience.