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Intelligent traffic light management using predictive and dynamic traffic flow analysis.

Kuldeep Nautiyal1, Durgaprasad Gangodkar1, Manoj Diwakar1

  • 1Department of CSE, Graphic Era Deemed to be University, Dehradun, India.

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Summary
This summary is machine-generated.

This study introduces an adaptive traffic light system (ATLS) that uses machine learning to predict traffic and dynamically adjust signals. The intelligent system significantly reduces waiting times and emissions in urban traffic.

Keywords:
Adaptive traffic light systemIntelligent traffic lightsSUMOTraffic simulation

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Area of Science:

  • Intelligent Transport Systems (ITS)
  • Machine Learning Applications
  • Urban Planning & Traffic Management

Background:

  • Traffic congestion poses significant challenges in urban areas, leading to increased pollution, fuel consumption, and accidents.
  • Intelligent Transport Systems (ITS) and Smart Traffic Light Systems (TLS) are crucial for improving urban mobility and infrastructure efficiency.
  • Adaptive Traffic Light Systems (ATLS) offer dynamic signal timing adjustments based on real-time traffic demand, outperforming fixed-time systems.

Purpose of the Study:

  • To propose an Adaptive Traffic Light System (ATLS) that integrates machine learning-based traffic volume prediction with a pressure-based control method.
  • To evaluate the effectiveness of the proposed ATLS in reducing traffic congestion and environmental impact.
  • To compare various machine learning algorithms for optimal traffic volume prediction within the ATLS framework.

Main Methods:

  • Developed an Adaptive Traffic Light System (ATLS) integrating hourly and daily traffic volume predictions using machine learning.
  • Employed a pressure-based method for dynamic traffic light phase control, responsive to current traffic conditions.
  • Utilized the Simulator of Urban Mobility (SUMO) for simulation-based evaluation of the ATLS at an isolated intersection.
  • Conducted a comparative analysis of Random Forest, K-Nearest Neighbors, Decision Tree, Gradient Boosting, and XGBoost for traffic prediction.

Main Results:

  • The proposed ATLS demonstrated an average reduction of 26.3% in average waiting time and 22.4% in average time loss.
  • Significant decreases were observed in total time loss (19.4%), average CO emissions (23.8%), and average CO2 emissions (17.4%).
  • The system's performance was validated across 12 diverse scenarios, outperforming existing methods.

Conclusions:

  • The proposed machine learning-integrated Adaptive Traffic Light System effectively manages urban traffic flow and reduces environmental impact.
  • Dynamic traffic signal control based on predictive modeling offers a superior solution for mitigating traffic congestion compared to traditional methods.
  • The study highlights the potential of advanced ITS solutions in creating more efficient and sustainable urban transportation networks.