Managing traffic congestion is crucial for improving mobility, reducing fuel consumption, and mitigating environmental impacts in urban areas. To address this challenge, we present a novel framework named TCP-ACO for detecting traffic congestion that classifies congestion into three distinct types: expected, unexpected, and real-time. The framework utilizes data from various sources, including databases, Ant colony optimization (ACO) systems, and computer vision techniques, to precisely detect and handle traffic congestion. Expected congestion is identified by analyzing historical traffic data and scheduled events, while unexpected congestion is detected by leveraging real-time data from ACO systems. Real-time congestion is detected by employing computer vision techniques, such as analyzing video footage from cameras or drones. The proposed framework has the potential, by recognizing and managing various types of congestion, to improve traffic flow, shorten travel times, and decrease environmental impacts. Additionally, it also offers a precise and effective solution for traffic congestion detection, which is a crucial aspect of smart city traffic management systems. Our analysis shows that the ACO algorithm adapted in TCP-ACO is more effective in finding the shortest path between two cities (result obtained: 4.014) compared to the result obtained from the shortest path technique compounded with computer vision (which yields a score of 6.224 when the path is free). This indicates the effectiveness of the proposed framework in addressing the challenges of traffic congestion, offering a promising solution for smart city traffic management systems to improve mobility and reduce environmental impacts in urban areas.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic Congestion Prevention Using Ant Colony Optimization


    Contributors:


    Publication date :

    2023-05-08


    Size :

    867214 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic-congestion prevention system, traffic-congestion prevention method, and recording medium

    ASHIDA YUTA / NISHIOKA ITARU | European Patent Office | 2020

    Free access

    TRAFFIC-CONGESTION PREVENTION SYSTEM, TRAFFIC-CONGESTION PREVENTION METHOD, AND RECORDING MEDIUM

    ASHIDA YUTA / NISHIOKA ITARU | European Patent Office | 2017

    Free access

    TRAFFIC-CONGESTION PREVENTION SYSTEM, TRAFFIC-CONGESTION PREVENTION METHOD, AND RECORDING MEDIUM

    ASHIDA YUTA / NISHIOKA ITARU | European Patent Office | 2018

    Free access

    Mitigating Traffic Congestion: Solving the Ride-Matching Problem by Bee Colony Optimization

    Teodorović, Dušan / Dell’ Orco, Mauro | Taylor & Francis Verlag | 2008