Monitoring bicycle and pedestrian traffic is crucial for enhancing urban safety and promoting active transportation.
MetroCount offers cutting-edge solutions for monitoring micromobility in urban and rural areas. Our systems accurately detect and classify bike types, e-scooters, and pedestrians. By deploying our sensors, cities gather vital data on usage patterns, including traffic volumes and peak times.
Building on the MetroCount-renowned accuracy and reliability, our RidePod systems enable informed urban planning by providing insights into peak usage, trends, and route preferences.
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Unmatched Accuracy
Outperforming competitor counters, the RidePod detects non-magnetic vehicles, such as carbon fibre bikes, and differentiates e-scooters or bicycles travelling in clusters, achieving 99% accuracy as verified by multiple independent tests.
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No Mains Power Required
As with any other MetroCount system, the RidePods are energy-efficient and use solar power to record data. When the remote access module is enabled, the same setup sends site diagnostics and traffic data to your Inbox or ATLYST account.
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Get The Full Picture
Between the RidePod and RoadPod ranges, we can record the entire traffic spectrum, from cars to e-scooters and everything else. Our consistent approach to data collection, traffic analysis and display gives you a clear view of traffic mode distribution across your network.
Discover Real-World Applications of Our Bike Counters
The Traffic Survey That Led Amsterdam to Ban Mopeds from Bike Paths
MetroCount traffic counter survey results lead to mopeds banned on cycle paths in Amsterdam.
In London, the Ealing Council leverages accurate MetroCount bike traffic data to secure grants and reach cycling participation levels similar to those in Denmark and the Netherlands.
A MetroCount traffic survey on a Paris shared path has changed the game in tube data collection by becoming the first survey of its kind to differentiate active travel modes.
Our RidePod BP bicycle counter was installed in East Melbourne to study whether motor traffic in the adjacent traffic lanes interferes with its sensor accuracy.
