Forecasting the Passage Time of the Queue of Highly Automated Vehicles Based on Neural Networks in the Services of Cooperative Intelligent Transport Systems

Vladimir Shepelev, Sultan Zhankaziev, Sergey Aliukov, Vitalii Varkentin, Aleksandr Marusin, Alexey Marusin and Aleksandr Gritsenko
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Vladimir Shepelev: Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia
Sultan Zhankaziev: Department of Road Traffic Management and Safety, Moscow Automobile and Road Construction State Technical University, 125319 Moscow, Russia
Sergey Aliukov: Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia
Vitalii Varkentin: Department of Aeronautical Engineering, South Ural State University (National Research University), 454080 Chelyabinsk, Russia
Aleksandr Marusin: Department of Transportation of the Academy of Engineering, Peoples’ Friendship University of Russia, 117198 Moscow, Russia
Alexey Marusin: Department of Transportation of the Academy of Engineering, Peoples’ Friendship University of Russia, 117198 Moscow, Russia
Aleksandr Gritsenko: Department of Machine-Tractor Fleet Operation, South Ural State Agrarian University, 457100 Troitsk, Russia

Mathematics, 2022, vol. 10, issue 2, 1-22

Abstract: This study addresses the problem of non-stop passage by vehicles at intersections based on special processing of data from a road camera or video detector. The basic task in this article is formulated as a forecast for the release time of a controlled intersection by non-group vehicles, taking into account their classification and determining their number in the queue. To solve the problem posed, the YOLOv3 neural network and the modified SORT object tracker were used. The work uses a heuristic region-based algorithm in classifying and measuring the parameters of the queue of vehicles. On the basis of fuzzy logic methods, a model for predicting the passage time of a queue of vehicles at controlled intersections was developed and refined. The elaborated technique allows one to reduce the forced number of stops at controlled intersections of connected vehicles by choosing the optimal speed mode. The transmission of information on the predicted delay time at a controlled intersection is locally possible due to the V2X communication of the road controller equipment, and in the horizontally scaled mode due to the interaction of HAV—the Digital Road Model.

Keywords: traffic signal control; machine learning; intelligent transport systems; unmanned vehicles (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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