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Ensuring that truck drivers take mandatory rest breaks is essential for improving road safety and preventing fatigue-related crashes. In this project, aÌýlarge-scale telematics (IAP) data of over-size over-mass heavy vehicles was used to infer driver rest-stop behaviour across Australia’s freight network. By applying an advanced machine learning method (Continuous Hidden Markov Model), we were able to automatically identify trip segments and classify millions of stops, such as rest breaks, loading and unloading, or refuelling, without relying on driver logbooks or manual surveys. The model successfully processed over 71 million GPS records, offering a powerful data-driven method for monitoring compliance with rest regulations. These insights help logistics operators, regulators, and road agencies better understand where and when heavy vehicle drivers rest, enabling targeted safety interventions, improved infrastructure planning, and more effective fatigue management policies.

CI's:ÌýDrÌýElnaz (Elli) Irannezhad & Mehdi Taghavi

Pedestrians often take the most direct route—known as a desire line—to switch between transport modes, even when this path is not the safest option. This project investigated why some people choose informal shortcuts over designated pedestrian routes at busy Melbourne public transport stations and intersections. Using more than 4,500 field observationsÌýand 424 intercept surveysÌýcollected over a week at two key sites—Queensbridge at CrownÌýand Russell & Bourke Streets—the study analysed how time of day, weather, gender, age, mobility, and other behavioural factors influence pedestrian decisions. Results showed that around one-third of pedestriansÌýused unsafe desire lines, especially during peak hours or when rushing to catch transport. The findings highlight the importance of pedestrian-centric designÌýin enhancing safety at public transport interchanges. The research was carried out by the University of NSW’ Research Centre for Integrated Transport Innovation (rCITI) for the Department of Transport Victoria.

Project Manager:ÌýDrÌýElnaz (Elli) Irannezhad

Empowering Safer Travel for Women After Dark in Greater Sydney

The ºÚÁÏÍø´óÊÂ¼Ç School of Civil and Environmental Engineering, in partnership with Cardno, led a groundbreaking initiative to improve safety for women travelling at night.

Commissioned by Transport for NSW as part of the Safety After Dark Innovation Challenge, the project was designed to help women make more informed travel choices and feel safer in transit environments after dark.

The research team developed a digital tool that quantified passive surveillance—the presence of open and active street-level businesses such as restaurants, shops, and services. These establishments contributed to a sense of safety by increasing visibility and reducing opportunities for crime.

The project introduced a Passive Surveillance Index, which scored streets based on business operating hours and other environmental factors. This index was integrated into a web and/or mobile application, enabling women to identify safer routes and areas with higher levels of passive surveillance.

By providing real-time, location-based safety insights, the tool empowered women to navigate the city with greater confidence and freedom. The final outcome was an action plan for Transport for NSW, offering practical recommendations for design, technology, and behavioural interventions to enhance safety across Greater Sydney.

This initiative marked a significant step toward inclusive, data-driven urban planning that prioritised the lived experiences of women in public spaces.

CI's:ÌýDr Meead Saberi & Linh Truong