The bike counter uses edge computing which NovuMind believes is paving the way for a new generation of smart and green cities
Norwegian Public Roads Administration (NPRA) has installed a smart bicycle counter on a major road in Trondheim to help inform the city’s green transportation policy.
The third largest city in Norway intends to invest more in bicycle infrastructures next year, which the government hopes to encourage more people to ride bicycles.
In the trial, the AI-powered device located on the side of Prinsens Gate reliably counted the number of bicycles on the road across a period lasting several hours.
“If all the bicyclists drive cars instead, there will be a 5km-long congestion in Trondheim”, said a spokesperson from Trondheim municipality. “We not only want the cyclists to continue riding bikes, but we also want more people to ride bikes. Not only the young, but also the elderly people and their grandchildren.”
Unlike previous measures NPRA has trialled, according to NovuMind, its smart bicycle counter is an example of a new wave of edge computing devices, where the AI capability is built into each single device.
Benchmarked against humans, it achieved an accuracy of 96.4 per cent, which is better than all previous solutions that the NPRA has tested.
“Edge intelligence will pave the way for a new generation of smart and green cities. We believe that edge intelligence is the best way to build scalable, future-proof, city- and country-scale systems,” said Scarlett Teng, AI engineer at NovuMind.
As cities implement new initiatives and policies to address climate change and other issues, data collected from existing infrastructures can become irrelevant or inadequate.
“Real-time information about traffic flow in cities is critical to intelligent optimisation of public transportation, safety, and emergency services,” added Ren Wu, founder and CEO of NovuMind.
“our sensor is a low-cost, versatile, non-invasive device that can be dynamically reconfigured to simultaneously locate, count, and track multiple different types of traffic flow, including automobiles, pedestrian, bicycle, and even animals.”
Detailed traffic data can then be continuously reported to system cloud servers with negligible load to existing networking, storage, and computing infrastructure because the high-bandwidth raw sensor data is processed on-device using state-of-the-art deep artificial neural networks powered by NovuMind in-house developed ASIC.
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