Adaptive Traffic Control With TinyML
Abstract
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.
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The implied method uses piezoelectric sensors embedded in many lanes of a road.
“The implied method uses piezoelectric sensors embedded in many lanes of a road.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Roshan et al., 2021
The deployed algorithm needs only 1.754 KB.
“The deployed algorithm needs only 1.754 KB.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Roshan et al., 2021
Automated traffic scheduling is recently investigated for possible integration with the TinyML framework to improve the real-time traffic system.
“Automated traffic scheduling is recently investigated for possible integration with TinyML framework to improve the real-time traffic system (Roshan et al., 2021).”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Roshan et al., 2021
The vehicles are then classified for prediction of green light timings.
“The vehicles are then classified for prediction of green light timings.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Roshan et al., 2021
The study uses a random forest regressor (RFR) to predict the duration of the signal based on the input vehicle count in each lane.
“The study uses a random forest regressor (RFR) to predict the duration of the signal based on the input vehicle count in each lane.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Roshan et al., 2021
A twopoint time ratio method is sought to detect a vehicle by using the piezo-sensors' data.
“A twopoint time ratio method is sought to detect a vehicle by using the piezo-sensors' data.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Roshan et al., 2021
The study is deployed on an Arduino Uno, augmented with the m2gen library with support from Scikit Learn.
“It is deployed on an Arduino Uno, augmented with the m2gen library with support from the Scikit Learn.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Roshan et al., 2021
Cites
TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities
Sanchez-Iborra, R., & Skarmeta, A. F. (2020) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2020.3005467 · Cited by 295 · see citing works
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A review on TinyML: State-of-the-art and prospects
Ray, P. P. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.11.019 · Cited by 460 · see citing works
TinyML Smart Sensor for Energy Saving in Internet of Things Precision Agriculture platform
Nicolas, C., Naila, B., & Amar, R.-C. (2022) · 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN) · doi:10.1109/icufn55119.2022.9829675 · Cited by 53 · see citing works
- Authors
- Roshan, A. N., Gokulapriyan, B., Siddarth, C., & Kokil, P.
- Affiliations
- Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram (IN)
- Venue
- 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)
- Publisher
- IEEE
- Published
- 2021-03-25
- Pages
- 451-455
- OpenAlex
- W3161834345
- Cited by
- 27
- References
- 1
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 9 · 2023: 5 · 2022: 7 · 2021: 1
- FWCI
- 8.21
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Transportation Systems and Logistics
- Keywords
- adaptive traffic control, TinyML, vehicle detection, piezoelectric sensors, traffic congestion
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