AI counts and recognizes all modes of transportation

Ilmakuva Kuokkalan sillasta ja Lutakon alueesta

Like many other cities, Jyväskylä is actively developing the measurement of different modes of transportation. Analyses are now being conducted using an AI-based solution.

New innovative solutions are constantly being developed for traffic flow measurement and analysis. One example is the monitoring of mobile devices, which is an excellent tool for tracking and interpreting larger mass movements. For instance, Google provides information on traffic flow and travel times between different points based on the movements of mobile devices.

However, when the goal is to accurately count traffic volumes and identify different modes of transportation, counting devices need to be installed on or near the roadway.

The latest advancements in this development include the use of neural networks and deep learning, also known as artificial intelligence. In this solution, an AI software is trained to recognize all observed traffic from video footage. The AI identifies, counts, and classifies vehicles and categorizes light traffic into cyclists and pedestrians. It also detects the direction and speed of the objects.

Jyväskylä takes a leap towards AI-powered measurement

Jyväskylä took its first step towards a new era of traffic measurement with artificial intelligence.

The city partnered with Nodeon to select one of its busiest thoroughfares for measurement. This location connects extensive residential areas east of Jyväskylä’s downtown with Lutakko, as well as the bypassing waterfront route and the city’s central downtown via Kuokkala Bridge. The goal was to count all traffic from the bridge.

Autoja ja kevyen liikenteen kulkijoita Kuokkalan sillalla — *AI identifies all modes of transportation from video footage.*

Traditional measurement techniques would struggle to measure this location. Traffic on the bridge flows in both directions, and on either side of the roadway, there are two heavily trafficked paths for pedestrians and cyclists. Traditional technologies would inevitably require combining various techniques and installing induction loops under the pavement, which is typically difficult on a bridge.

Nodeon approached the challenge using just one camera. Located at the measurement station, an AI-powered camera analysis device continuously analyzes traffic across the entire bridge, identifies and categorizes vehicles and light traffic, and transmits all data in real-time to Nodeon’s centralized Smart City data storage and analysis environment.

AI provides a truly viable, in some cases superior alternative to traditional traffic flow measurement techniques!

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