Count every mode, at more sites, for the same budget
Loops, tubes and manual surveys count vehicles well and everyone else badly. A Count Pod gives you continuous, classified counts of pedestrians, cyclists, e-scooters and vehicles, with direction and speed, from one device. It installs in under two hours and needs no grid power, no civil works and none of the approvals that come with them.
You have good data on vehicles and very little on anyone else. Business cases for walking and cycling investment rely on periodic surveys, mode-shift targets get reported without a baseline, and each new count site brings a power connection, a traffic management plan and months of approvals with it. Coverage of the network is limited by process as much as by budget.
We map your sites, choose Count Pods or CCTV integration per location, and fit each pod with a lens for its mounting height.
Solar Count Pods need no mains power or trenching. CCTV integration runs on a lightweight CPU server inside your network.
Our engineering team loads a custom AI model for each site, then calibrates, tunes and ground-truths it against manual counts before go-live. All of it is done remotely.
Pulse Analytics dashboards, automated reports and the REST API put the data where your team already works.
Count Pods on existing poles at intersections, parks and shared paths. After commissioning, calibration, model updates and monitoring all happen remotely.
Transport data on its own tells you what happened on a corridor. City Pulse puts it beside the other things that move a city: events, cruise calls, weather, construction and the state of the road network.
Sensors under shop awnings on business power supplies, classifying pedestrians, share bikes and cargo bikes on a busy retail street through a separated cycleway trial.
Cycling movements are running 25 to 30% above the same month a year earlier. “Monitoring these trends in real time has proven to be a game-changer.” Whangārei District Council
14 sensors kept counting through a NZ$100m streetscape upgrade, with no retraining as the scene changed weekly.
