Estimation of the Unidirectional Traffic Flow Velocity Limit with High Computational Efficiency
Year: 2025
Type: article
Abstract: Introduction. In the modern development of intelligent transportation systems (ITS), an urgent task is the accurate estimation of the velocity limit of traffic flow on a highway. Despite existing solutions to this problem based on statistical mechanics methods and stochastic models, gaps remain in adapting these theories to real road segments of limited length. The traditional thermodynamic limit formula, used to calculate the average velocity of traffic flow, becomes inaccurate for small road segment lengths, limiting its applicability in practical traffic monitoring tasks. The aim of this study is a comparative analysis of various approaches to estimating the average velocity limit of traffic flow. Materials and Methods. The study was conducted using the method of statistical mechanics and a stochastic model on a one-dimensional finite lattice. Numerical experiments with various parameter values (number of cells, traffic density, and movement probability) were used for analysis. Results. The study revealed significant discrepancies between the results obtained using the statistical mechanics method and other approaches when the road segment length was small. The efficiency of the second and third approaches was confirmed for limited road segments, where they demonstrated greater accuracy and applicability. Discussion and Conclusion. The research results have practical significance for the development of intelligent traffic management systems, especially for short road segments. The proposed approaches can be successfully integrated into modern monitoring systems to improve their accuracy. The theoretical significance of this work lies in advancing the methodology for traffic flow estimation while accounting for the specific conditions of real-world environments.
Author: I. A. Kuteynikov
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409273554