E-Bike Detection System with AI for tourist flows
Scenario
A mountain municipality located in an eco-friendly area, where e-bike transportation is one of the main attractions offered to tourists and visitors.
In these areas, climate change has resulted in less and less snow, reducing the “white” season in favor of the “green” season. In addition, high-altitude trails are notorious for poor connectivity, lack of charging stations, and limited food and beverage outlets.
Improving tourism supply and encouraging green mobility
The main challenge involved gathering information on tourist flows to improve guest services and incentivize guests to opt for more sustainable mobility.
An AI solution to monitor the passage of e-bikes
To achieve these goals, an innovative AI solution based on advanced audio analysis techniques was able to isolate the distinctive sound produced by the electric motor of e-bikes, differentiating it from that of other vehicles.
This software module, added to a prototype board equipped with analog and digital microphones and other sensors – STM’s (STMicroelectronics) STEVAL-STWINKT1B – resulted in an efficient, removable and weatherproof monitoring system capable of identifying the areas of greatest e-bike passage and, therefore, the best locations to implement specific services (commuter parking, charging stations, refreshment points, etc.).
Enhance services, strengthen sustainability and enrich the experience
This method proved effective in detecting e-bikes and tracking tourists’ movements in the target context. In addition, the spectrogram analysis and distinctive pulses associated with e-bikes provided valuable information on route popularity and cyclist preferences.
In addition, the dynamic monitoring dashboard for data analysis enabled our clients to extract useful data to make informed decisions, enhance municipality services, strengthen sustainable mobility, and improve the tourism experience.
