Temat: traffic jam - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A computational model for multi-criteria decision making in traffic JAM problem
Autorzy:: Naeem, Ali
Abbas, Jabbar
Powiązania:: https://bibliotekanauki.pl/articles/2141883.pdf
Data publikacji:: 2021
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: multi-criteria decision making
fuzzy measure
choquet integral
traffic jam
Opis:: In this paper, we apply a computational model for multicriteria decision making in traffic jam problems. First, we propose a system to determine the optimal shortcut road by reading the number of cars in each street using Radio Frequency Identification (RFID). Then, we have processed the data of traffic jam problems using Choquet integral with writing algorithm and computer program as a working procedure.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 2; 39-43
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

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

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