Wellington City Council is delivering a massive increase in active transport infrastructure over the coming decade, with work underway to upgrade their network from 23Km as of 2021, to 166km of connected cycleways. To deliver the necessary insights, Wellington has chosen to deploy a novel Computer Vision monitoring network of over seventy Smart Sensors across the city. This case study will review the impact of novel data sets from computer vision sensors is having on management and decision making for their transport infrastructure; plans for next steps, and lessons learnt from the project to date.

This study investigates the potential for video analytics to bridge the gap between traffic safety and efficiency. Combining AI-powered safety analytics with edge computing could result in powerful adaptive signal phasing optimisation algorithms to jointly optimise safety and efficiency at signalised intersections in real time. Opportunities arise to eliminate "ghost traffic jams" by smartly deciding between protective and permissive phasing and reducing right-turn conflicts by 90%.

The Road Safety Technology team at Transport for NSW used AI to develop a mobile Road Safety Incident Investigation Camera (ROSIICAM) to detect nearmisses. ROSIICAM was deployed at ten sites in 2022 and 2023 in various road settings, including a roundabout, a high-speed highway and urban locations with vulnerable road users and light rail. This technology can assist in the development of customised and effective road safety treatments at specific locations to help reduce road trauma.