Predicting the Traffic Capacity of an Intersection Using Fuzzy Logic and Computer Vision

Vladimir Shepelev, Alexandr Glushkov, Tatyana Bedych, Tatyana Gluchshenko and Zlata Almetova
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Vladimir Shepelev: Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia
Alexandr Glushkov: Department of Mathematical and Computer Modelling, South Ural State University (National Research University), 454080 Chelyabinsk, Russia
Tatyana Bedych: Department of Energy and Mechanical Engineering, M. Dulatov Kostanay Engineering and Economic University, Kostanay 110000, Kazakhstan
Tatyana Gluchshenko: Department of Electric Power, Kostanay Regional University named after A. Baitursynov, Kostanay 110000, Kazakhstan
Zlata Almetova: Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia

Mathematics, 2021, vol. 9, issue 20, 1-19

Abstract: This paper presents the application of simulation to assess and predict the influence of random factors of pedestrian flow and its continuity on the traffic capacity of a signal-controlled intersection during a right turn. The data were collected from the surveillance cameras of 25 signal-controlled intersections in the city of Chelyabinsk, Russia, and interpreted by a neural network. We considered the influence of both the intensity of the pedestrian flow and its continuity on the traffic capacity of a signal-controlled intersection in the stochastic approach, provided that the flow of vehicles is redundant. We used a reasonably minimized regression model as the basis for our intersection models. At the first stage, we obtained and tested a simulated continuous-stochastic intersection model that accounts for the dynamics of traffic flow. The second approach, due to the unpredictability of pedestrian flow, used a relevant method for analysing traffic flows based on the fuzzy logic theory. The second was also used as the foundation to build and graphically demonstrate a computer model in the fuzzy TECH suite for predictive visualization of the values of a traffic flow crossing a signal-controlled intersection. The results of this study can contribute to understanding the real conditions at a signal-controlled intersection and making grounded decisions on its focused control.

Keywords: traffic capacity of an intersection; pedestrian flow; traffic simulation; fuzzy logic method; predictive visualization of a vehicle flow (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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