Application of calibrated vehicle dynamic indicators in city traffic management

Loktionova, Alina; Novikov, Aleksandr; Shevtsova, Anastasia · 2023 · Crossref

DOI: 10.1051/e3sconf/202341305010

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study addresses the need to update vehicle dynamic indicators used in urban traffic management, motivated by significant changes in the Russian automotive fleet over recent decades. As passenger vehicles have improved in power, design, and acceleration, traditional models based on older "conditional" car parameters no longer accurately reflect modern traffic dynamics. The authors aim to define parameters for a "calibrated" car that better represents current traffic flows, thereby improving the accuracy of traffic control systems and road design calculations. The research was conducted in Belgorod, Russia, combining statistical analysis of vehicle sales data from 2005 to 2022 with full-scale field studies of traffic flows at various intersection types. The analysis identified the most common vehicle brands, including Lada Granta, Kia Rio, and Renault Logan, which constitute the majority of the 16,000 daily vehicles. Mathematical calculations were performed to determine dynamic characteristics—specifically speed, acceleration, tangential thrust, and dynamic factor—for these prevalent models. The study derived a mathematical model to calculate the average acceleration of a calibrated vehicle across different gear ratios, resulting in an overall acceleration value of 1.42 m/s². This new indicator was compared against the traditional conditional car parameters to assess its impact on traffic flow metrics. The results demonstrate a 9.23% difference between the dynamic characteristics of the calibrated car and the previously used conditional car, indicating a shift in traffic physics. This adjustment significantly affects saturation flow calculations, which determine maximum lane capacity at traffic lights. Using the calibrated car parameters, the study established a more accurate saturation flow value of 2070 units per hour. Furthermore, the degree of saturation was found to be reached with five cars rather than the seven cars previously estimated. When applied to ten intersections along Bogdan Khmelnitsky Avenue, the calibrated model adjusted traffic light control cycle durations, with deviations ranging from -1.82% to 6.41% compared to classical calculations. The average deviation in cycle duration was 3.45%. The significance of this work lies in its contribution to more precise urban transport system management. By incorporating modern vehicle dynamics into traffic control algorithms, the study shows that transport delays at intersections can be reduced by an average of 30%. The findings suggest that updating primary data inputs in traffic management methods is essential for optimizing signal timing and road design, ensuring that infrastructure planning reflects the actual performance capabilities of contemporary vehicle fleets.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• traffic density