Case studies and real-world examples.
See VivaCity in action.

The Covid-19 pandemic changed public space use, leading the City of Melbourne to run its 2021 ‘Reimagining the City’ challenge. One winning project introduced a new bench design to foster social connections. Learn how Melbourne used VivaCity sensors and dwell time data to evaluate its impact.

Wellington City Council are using VivaCity sensors to gather highly sophisticated and anonymous active transport insights, which will be used to inform future interventions that promote walking and cycling in the city.

Waka Kotahi New Zealand Transport Agency has partnered with VivaCity in a world-first near miss study to help improve the understanding of road safety for their most vulnerable road users.

The New York City Department of Transportation expanded its network of Viva sensors to better understand how citizens use streets and inform safer designs.

VivaCity’s AI-powered traffic sensors have been deployed in the City of Port Phillip in Australia to measure bike rider, pedestrian and motor vehicle movements and help the council reduce congestion and increase sustainable transport.

Real-time traffic data enabled Brighton and Hove to justify lane reallocations, securing strong public approval for a safer, greener active travel corridor.

Liverpool City Region leveraged multimodal data to evaluate cycleway usage, build robust business cases, and secure funding for ongoing active transport investments.

TfWM utilized near-miss data to identify dangerous traffic interactions, enabling proactive infrastructure improvements to protect vulnerable road users across the region.

Transport for Greater Manchester analysed aggregated pedestrian data from VivaCity sensors to understand out-of-hours city activity and build the evidence for a 7-day railway.

Glasgow City Council deployed AI sensors at key intersections to enhance cycle and vehicle detection, improving traffic signal responsiveness and overall road safety.

Westminster City Council utilised VivaCity sensors to monitor trial School Streets, providing clear evidence that temporary restrictions successfully reduced peak-time motorised traffic.

Coventry deployed AI sensors to continuously monitor multimodal traffic patterns, gathering robust, real-time data to evaluate scheme success and reduce urban congestion.

Nottingham implemented AI-optimised traffic signals to prioritise public transport, successfully reducing bus delays and improving journey reliability without disrupting wider traffic flow.

ontinuous traffic monitoring helped West Sussex evaluate their School Streets initiative, proving reductions in vehicle dominance and demonstrating increased active travel safety.