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Heterogeneous Algorithm for Efficient-Path Detection and Congestion Avoidance for a Vehicular-Management System.

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Summary
This summary is machine-generated.

This study introduces ACO-PT, a novel algorithm for smart city traffic management. It enhances efficient routing, reduces energy consumption, and improves data throughput in urban environments.

Keywords:
ant-colony optimizationcongestion avoidanceend-to-end delayenergy consumptionpheromone termite

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Area of Science:

  • Computer Science
  • Transportation Engineering
  • Artificial Intelligence

Background:

  • Megacity traffic congestion is a persistent problem, exacerbated by population and vehicle growth.
  • Existing routing algorithms require improvement for efficiency, energy use, and latency.
  • Smart city initiatives, particularly the Internet of Vehicles (IoV), offer potential solutions.

Purpose of the Study:

  • To develop an efficient routing algorithm for urban traffic management.
  • To improve energy efficiency, increase data throughput, and reduce end-to-end latency.
  • To address vehicle congestion and enhance automatic vehicle detection.

Main Methods:

  • A heterogeneous algorithm, Ant-Colony Optimization with Pheromone Termite (ACO-PT), was developed.
  • ACO-PT integrates Ant-Colony Optimization (ACO) and Pheromone Termite (PT) algorithms.
  • A congestion-avoidance module and an automatic-vehicle-detection (AVD) module were incorporated.

Main Results:

  • The ACO-PT algorithm demonstrated superior performance in experimental simulations.
  • Compared to three state-of-the-art algorithms, ACO-PT showed significant improvements.
  • Key performance metrics improved: energy usage, end-to-end delay, and throughput.

Conclusions:

  • The proposed ACO-PT algorithm effectively addresses efficient routing in urban areas.
  • ACO-PT offers a promising solution for mitigating traffic congestion and improving IoV systems.
  • The algorithm's enhancements in energy efficiency, latency, and throughput validate its effectiveness.