Automatic traffic scheduling can improve the efficiency of the traffic system by decreasing significant delays and easing congestion. This paper introduces a machine learning (ML) based algorithm to automate the traffic scheduling based on the density of vehicles waiting, without any human intervention. Presently, the traffic signal timers are preset which is unreasonable for a stochastic process like vehicular traffic. The proposed methodology detects vehicles with piezoelectric sensors embedded across each lane. The two-point time ratio method is utilized to identify the vehicles using the sensor's data and the vehicles are classified based on their pick-up speed. Further, a TinyML based model is proposed to predict the green signal timings.
Adaptive Traffic Control With TinyML
25.03.2021
730117 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
TinyML Artificial intelligence IoV system based on TinyML
Europäisches Patentamt | 2024
|TinyML Artificial intelligence IoV system based on TinyML
Europäisches Patentamt | 2025
Modified AMBER Alerts System Using TinyML Processing
Springer Verlag | 2023
|TinyML-based traffic safety system for assisting pedestrians to pass pedestrian crosswalk
Europäisches Patentamt | 2022
|