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    Wyświetlanie 1-4 z 4
    Tytuł:
    An adaptative system for signposted intersection control : ASSINC
    Autorzy:
    Elkosantini, S.
    Mnif, F.
    Chabchoub, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/91830.pdf
    Data publikacji:
    2012
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    adaptative system
    signposted
    ASSINC
    case-based reasoning
    CBR
    traffic signal
    traffic
    Opis:
    This paper deals with the development of intelligent and adaptative system for signposted intersection control. The role of such systems is to manage the existing infrastructure to ease congestion and respond to crises. The proposed system, named ASSINC, try to insure a more fluid traffic flow. ASSINC is based on case based reasoning (CBR) approach and fuzzy logic to consider imprecise information taken from some detector. In fact, the CBR is always considered as a cyclic paradigm of Artificial Intelligence and that is used to learning and problem solving based on past experience. The developed system is tested on a virtual junction and the obtained results are discussed.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 1; 21-29
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Classification of traffic signal system anomalies for environment tests of autonomous vehicles
    Autorzy:
    Lengyel, H.
    Szalay, Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/112021.pdf
    Data publikacji:
    2018
    Wydawca:
    Stowarzyszenie Menedżerów Jakości i Produkcji
    Tematy:
    classification
    traffic signal system
    anomalies
    autonomous vehicles
    test environment
    pojazd autonomiczny
    znaki drogowe
    sygnalizacja świetlna
    Opis:
    In the future there will be a lot of changes and development concerning autonomous transport that will affect all participants of transport. There are still difficulties in organizing transport, but with the introduction of autonomous vehicles more challenges can be expected. Recognizing and tracking horizontal and vertical signs can cause a difficulties for drivers and, later, for autonomous systems. Environmental conditions, deformity and quality affect the perception of signals. The correct recognition results in safe travelling for everyone on the roads. Traffic signs are designed for people that is why the recognition process is harder for the machines. However, nowadays some developers try to create a traffic sign that autonomous vehicles can use. Computer identification needs further development, as it is necessary to consider cases where traffic signs are deformed or not properly placed. In the following investigation, the advantages and disadvantages of the different perception methods and their possibilities were gathered. A methodology for the classification of horizontal and vertical traffic signs anomalies that may help in designing better testing and validation environments for traffic sign recognition systems in the future was also proposed.
    Źródło:
    Production Engineering Archives; 2018, 19; 43-47
    2353-5156
    2353-7779
    Pojawia się w:
    Production Engineering Archives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Genetic algorithm application for optimizing traffic signal timing reflecting vehicle emission intensity
    Autorzy:
    Hai, Dinh Tuan
    Manh, Do Van
    Nhat, Nguyen Minh
    Powiązania:
    https://bibliotekanauki.pl/articles/2098075.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    traffic signal optimization
    heuristic solution
    genetic algorithm
    vehicle exhaust emission
    intelligent transport system
    optymalizacja sygnalizacji drogowej
    rozwiązanie heurystyczne
    algorytm genetyczny
    emisja spalin pojazdu
    inteligentny system transportowy
    Opis:
    Urbanization has created continuous growth in transportation demand, leading to serious issues, including infrastructure overload, disrupted traffic flow, and associated vehicular emissions. As a result, resolving these problems has become one of the primary missions of governments worldwide. The optimization of the traffic signal timing system is considered a promising approach to overcoming the negative consequences of increasing vehicle volume. In metropolises, oversaturated intersections, where the traffic density and vehicle exhaust emission levels are significant, have been considered as the priority to target. Several scientists have attempted to design traffic lights with the most appropriate timing. However, the majority of previous studies have not formed a comprehensive evaluation of essential factors, especially regarding the appropriate weighting of vehicle emission parameters. By assessing the all-inclusive relationship of critical elements with an emphasis on vehicle exhaust emissions, a performance index model using a genetic algorithm (GA) is established in this paper, demonstrated by data from a case study in Taiwan.
    Źródło:
    Transport Problems; 2021, 16, 1; 5--16
    1896-0596
    2300-861X
    Pojawia się w:
    Transport Problems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    AI-based Yolo v4 intelligent traffic light control system
    Autorzy:
    Prathap, Boppuru Rudra
    Kumar, Kukatlapalli Pradeep
    Chowdary, Cherukuri Ravindranath
    Hussain, Javid
    Powiązania:
    https://bibliotekanauki.pl/articles/27314354.pdf
    Data publikacji:
    2022
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    traffic jam
    traffic light system
    traffic management
    intelligent monitoring
    signal switching algorithm
    artificial intelligence
    Opis:
    With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, wait times for commuters at traffic signal points are not reduced. The proposed methodology employs artificial intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in shorter vehicle waiting times.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 4; 53--61
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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