Improvement of traffic flow management at intersections using cluster analysis and fuzzy logic

Signal-controlled intersections of urban transport highways are the busiest and most problematic places in the urban road network because of vehicle delays at intersections reducing their traffic capacity, as well as a sharp change in the nature of traffic (braking-waiting-acceleration), which worsens the environmental situation. This paper develops a mathematical model for calculating the recommended vehicle speed at signal-controlled intersections for non-stop passage, in order to increase the traffic capacity and safety, taking into account various factors. The authors carry out a preliminary single-factor analysis of the influence of the length of the space interval between intersections, the operating time of traffic light cycles, the number of cars in the queue, the road surface condition, and the category of the most inertial vehicles. The study involved a cluster analysis, which allowed identifying groups of lanes at intersections with similar traffic characteristics. The authors derived coefficients for the quantitative assessment of the influence of various factors on the recommended speed: the coefficient of queue passing dynamics (kd), which takes into account the road surface condition, and the coefficient of vehicle inertia in the queue (ki). The analysis showed that changes in the road surface conditions reduce the speed by 20-40%, an increase in the number of cars in the queue by more than five units reduces the speed by 15-30%, and the presence of vehicles of categories III (trucks from 3.5 to 12 tons) and IV (trucks over 12 tons) in the queue reduces the recommended speed by 10-25%. The multi-factor approach is based on the fuzzy logic method, which allows taking into account probabilistic fluctuations in the input parameters and their interrelationships. The obtained results can be used to optimize urban traffic, reduce the number of stops before intersections, increase the traffic capacity of signal-controlle.

Authors

Institutions

Publication Details

Journal
FME Transaction
Published
2025-01-01
DOI
https://doi.org/10.5937/fme2503499s
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improvement of traffic flow management at intersections using cluster analysis and fuzzy logic

Olga Fadina, Vladimir Shepelev, Aleksandr Glushkov
FME Transaction
Traffic Prediction and Management Techniques
article

Improvement of traffic flow management at intersections using cluster analysis and fuzzy logic

Olga Fadina, Vladimir Shepelev, Aleksandr Glushkov
article en

Abstract

Signal-controlled intersections of urban transport highways are the busiest and most problematic places in the urban road network because of vehicle delays at intersections reducing their traffic capacity, as well as a sharp change in the nature of traffic (braking-waiting-acceleration), which worsens the environmental situation. This paper develops a mathematical model for calculating the recommended vehicle speed at signal-controlled intersections for non-stop passage, in order to increase the traffic capacity and safety, taking into account various factors. The authors carry out a preliminary single-factor analysis of the influence of the length of the space interval between intersections, the operating time of traffic light cycles, the number of cars in the queue, the road surface condition, and the category of the most inertial vehicles. The study involved a cluster analysis, which allowed identifying groups of lanes at intersections with similar traffic characteristics. The authors derived coefficients for the quantitative assessment of the influence of various factors on the recommended speed: the coefficient of queue passing dynamics (kd), which takes into account the road surface condition, and the coefficient of vehicle inertia in the queue (ki). The analysis showed that changes in the road surface conditions reduce the speed by 20-40%, an increase in the number of cars in the queue by more than five units reduces the speed by 15-30%, and the presence of vehicles of categories III (trucks from 3.5 to 12 tons) and IV (trucks over 12 tons) in the queue reduces the recommended speed by 10-25%. The multi-factor approach is based on the fuzzy logic method, which allows taking into account probabilistic fluctuations in the input parameters and their interrelationships. The obtained results can be used to optimize urban traffic, reduce the number of stops before intersections, increase the traffic capacity of signal-controlle.

FME TransactionVol. 53(3)
South Ural State University (RU)
Russian Science Foundation
Openalex Percentile: Top 80%
Traffic Prediction and Management Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Improvement of traffic flow management at intersections using cluster analysis and fuzzy logic — Olga Fadina, Vladimir Shepelev, et al. · FME Transaction (2025) | TGRS Research Map | TGRS