Cycling is synonymous with daily European life—especially in Amsterdam, where 400,000 cyclists ride to their destinations every day. However, where there are cities full of cyclists, there’s congestion. Our Dutch partner, Multisensors, asked Ewald Dijkstra, Senior Advisor of Mobility Research at the City of Amsterdam, what challenges they face in the field of bicycle mobility, and why they chose the Signum bicycle counter to help solve the problem.
THE TASK:
Quality bicycle data collection. Essential to solving congestion.
Dijkstra pointed out that while motorized traffic is fairly well known, there is very little data on bicycle traffic, and what does exist—in the way of data—consists mostly of snapshots. He explained that the tech used so far, such as induction loops or pneumatic tubes, are unreliable or too fragile for permanent use. The Amsterdam municipality needs an accurate picture (as opposed to snapshots), based on reliable data, of the actual bicycle congestion across the city. The challenge is to understand the bottlenecks and make the cycle paths safer by optimizing the flow of cyclists.
THE TEST:
Studies conducted. Radars tested.
Dijkstra conducted a comparative study at a test site and found that the Signum radar was more reliable than manual counting. The accuracy is well over 90%, even during peak hours and bicycle congestion. The data (volume, speed, direction) is naturally anonymous and accessible online in real time. A game, and accurate-data-collecting-changer.
THE TECH:
Icoms Signum Radars. Doppler and Lidar tech.
The tech behind Icoms Signum is the TMA-3B3, designed and manufactured in Belgium by Icoms Detections, with multi sensors that integrate into a customized housing. This sensor combines a Doppler radar and a lidar. The lidar helps separate individual bicycles from groups, making the product particularly accurate in counting.
The radar communicates with a modem via an RS-232 link, and a solar panel makes it autonomous and functional 24/7/365, enabling continuous measurement. This allows the city of Amsterdam to understand the relative ridership at the various measurement points, considering external factors influencing bicycle traffic, such as periodic events or weather conditions.
https://www.multisensors.nl/klantcase/gemeente-amsterdam/