Positioning of Rolling Stock Based on Fuzzy Logic Algorithms for Detecting Transport Movement Anomalies

Yulia Fomenko; Anatoly Khomonenko

doi:10.20295/2413-2527-2025-343-77-86

VAK Russia 1.2.2 UDC 681.51 VAK Russia 2.9.8

3 more Collapse

POSITIONING OF ROLLING STOCK BASED ON FUZZY LOGIC ALGORITHMS FOR DETECTING TRANSPORT MOVEMENT ANOMALIES Positioning of Rolling Stock Based on Fuzzy Logic Algorithms for Detecting Transport Movement Anomalies

АА Аа

Published in Intellectual Technologies on Transport · Issue 3 · Pages 77–86 · Rubric: MATHEMATICAL MODELLING AND SYSTEM ANALYSIS

DOI: https://doi.org/10.20295/2413-2527-2025-343-77-86 · EDN: NRDYHD

Received: 05.08.2025 Accepted: 08.08.2025 Published: 25.09.2025

Authors

Yulia Fomenko

Anatoly Khomonenko

Yulia Fomenko

Emperor Alexander I St. Petersburg State Transport University Information and Computing System Department · Russian Federation · St. Petersburg

Anatoly Khomonenko

Academic degree: doctor of technical sciences

Science rang: professor

Emperor Alexander I St. Petersburg State Transport University Russian Federation · St. Petersburg

Mozhaisky Military Aerospace Academy Professor · Department of Mathematical and Software Engineering · Russian Federation · Saint Petersburg

ABSTRACT

Real-time detection of transport movement anomalies has attracted significant attention, since railway transport remains a key element of global logistics and passenger transportation. In the context of increasing traffic intensity, rising speeds and more stringent safety requirements, the identification of anomalies in the movement of rolling stock has become a critical task. Purpose: the selection of an algorithm for the positioning of rolling stock based on fuzzy logic (Takagi — Sugeno or Mamdani) is a justified choice in the event of a movement anomaly, as is the determination of the risk level of anomaly occurrence. Results: computerized scenario experiments have been conducted with the considered input data characterizing the main movement parameters (speed, vibration, acceleration, weather conditions, track condition) to ensure safety on railway sections. Discussion: the experimental results demonstrated the efficacy of the Takagi — Sugeno algorithm in determining the risk level of movement anomalies.

KEYWORDS

positioning, object coordinates, transport facilities, movement anomalies, fuzzy logic, traffic safety, Takagi — Sugeno algorithm, Mamdani algorithm

1. Kazyuk S. B. Analiz sistem pozitsionirovaniya podvizhnogo sostava [Analysis of rolling stock positioning systems], Sbornik tezisov dokladov nauchnoy konferentsii studentov i aspirantov Lipetskogo gosudarstvennogo tekhnicheskogo universiteta [Collection of abstracts of reports of the scientific conference of students and postgraduates of Lipetsk State Technical University], Lipetsk, Russia, June 29, 2012. Vol. 2. Lipetsk, Lipetsk State Technical University, 2012, Pp. 42–44. (In Russian) EDN: https://elibrary.ru/MIBBEM

2. Tumanova N. V., Bavrina A. A., Medvedeva V. M. Klassifikatsiya narusheniy bezopasnosti dvizheniya na zheleznodorozhnom transporte [Classification of Traffic Safety Violations in Rail Transport], Tekhnosfernaya bezopasnost gorodskikh aglomeratsiy: Sbornik materialov II Mezhdunarodnoy shkoly-konferentsii [Technosphere safety of urban agglomerations: Collection of materials of the II International school-conference], Moscow, Russia, December 15–16, 2021. Moscow, Russian University of Transport, Russian Open Academy of Transport, 2022, Pp. 275–280. (In Russian) EDN: https://elibrary.ru/BOQNWC

3. Kalacheva O. A., Pritsepova S. A. Analiz narusheniy bezopasnosti dvizheniya poezdov [Analysis of violations of train traffic safety], Aktualnye problemy zheleznodorozhnogo transporta: sbornik statey nauchnoy konferentsii [Actual problems of railway transport: collection of articles from a scientific conference], Voronezh, Russia, October 01, 2018. Voronezh, Voronezh Branch of Rostov State Transport University, 2018, Pp. 96–100. (In Russian) EDN: https://elibrary.ru/YLYHYL

4. Sayfutdinov A. V. Vyyavlenie anomaliy trafika v informatsionnykh sistemakh s ispolzovaniem metodov Machine Learning [Detection of Traffic Anomalies in Information Systems Using Machine Learning Methods], Perspektivy nauki [Science Prospects], 2023, No. 8 (167), Pp. 53–58. (In Russian) EDN: https://elibrary.ru/ZJYMIC

5. Shevchuk V. G., Levtrinsky V. V., Kryukova D. K. Podsistema monitoringa zheleznodorozhnogo mosta kak vazhnaya sostavlyayushchaya sistemy sputnikovogo monitoringa infrastruktury zheleznoy dorogi [Subsystem for monitoring a railway bridge as an important component of the satellite monitoring system for railway infrastructure], Stroitelstvo i vosstanovlenie iskusstvennykh sooruzheniy: materialy V Mezhdunarodnoy nauchno-prakticheskoy konferentsii [Construction and restoration of artificial structures: Proceedings of the V International Scientific and Practical Conference], Gomel, Belarus, April 21–22, 2016. Vol. 1. Gomel, Belarusian State University of Transport, 2016, Pp. 325–330. (In Russian) EDN: https://elibrary.ru/PDQTTG

6. Bezruchko V. V. Razrabotka avtomatizirovannoy sistemy kontrolya i diagnostirovaniya ustroystv peredachi informatsii zheleznodorozhnogo transporta [Development of an automated system for monitoring and diagnosing railway information transmission devices]: Abstract of the diss. on competition of a scientific degree PhD (Engin.). Moscow, Russian State Open Technical University of Transport, 1997, 24 p. (In Russian) EDN: https://elibrary.ru/ZKJOHT

7. Fomenko Yu. S., Khomonenko A. D. Opredelenie koordinat transportnogo obekta pri defitsite signalov s pomoshchyu modeli nechetkogo vyvoda Takagi — Sugeno [Detection of Transport Object Coordinates Under Signal Scarcity Using Takagi — Sugeno Fuzzy Inference Model], Intellektualnye tekhnologii na transporte [Intellectual Technologies on Transport], 2025, No. 1 (41), Pp. 46–55. DOI:https://doi.org/10.20295/2413-2527-2025-141-46-55. (In Russian) EDN: https://elibrary.ru/POQLHE

8. Wróbel J., Bury P., Zając M., et al. Fault Detection and Diagnostic Methods for Railway Systems — A Literature Survey, Advances in Science and Technology Research Journal, 2024, Vol. 18, Iss. 6, Pp. 361–391. DOI:https://doi.org/10.12913/22998624/191762. EDN: https://elibrary.ru/ALDDEK

9. Efimov V. V., Khomonenko A. D. (eds.). Tekhnologii iskusstvennogo intellekta: uchebnik [Artificial Intelligence Technologies: textbook]. Saint Petersburg, Mozhaisky Military Aerospace Academy, 2024. (In Russian)

JATS XML

Editor-in-Chief

Anatoly Khomonenko

khomon@mail.ru

Deputy Editor-in-Chief

Lesya Bozhko

Principal Contact

Maksim Orzhevsky

206@it-pgups.ru

Editorial Staff

Staff Editorial

itt-pgups@yandex.ru +7(812)457-86-06

1029

downloads

GOST

Fomenko Y. S., Khomonenko A. D. Positioning of Rolling Stock Based on Fuzzy Logic Algorithms for Detecting Transport Movement Anomalies // Intellectual Technologies on Transport. 2025. no. 3. pp. 77-86. DOI: https://doi.org/10.20295/2413-2527-2025-343-77-86. EDN: NRDYHD. (Date of access 28.09.2026).

MLA

Yulia Fomenko, Khomonenko A. D.. "Positioning of Rolling Stock Based on Fuzzy Logic Algorithms for Detecting Transport Movement Anomalies." Intellectual Technologies on Transport.3 (2025): 77-86. DOI: https://doi.org/10.20295/2413-2527-2025-343-77-86. EDN: NRDYHD.

APA

Fomenko Y. S., Khomonenko A. D. (2025). Positioning of Rolling Stock Based on Fuzzy Logic Algorithms for Detecting Transport Movement Anomalies. Intellectual Technologies on Transport(3), 77-86. https://doi.org/10.20295/2413-2527-2025-343-77-86 EDN: NRDYHD.

Chicago

Yulia Fomenko, Khomonenko A. D.. "Positioning of Rolling Stock Based on Fuzzy Logic Algorithms for Detecting Transport Movement Anomalies." Intellectual Technologies on Transport, no. 3 (2025): 77-86. https://doi.org/10.20295/2413-2527-2025-343-77-86. EDN: NRDYHD.

WIKI

{{cite journal | author = Fomenko Y. S., Khomonenko A. D. | title = Positioning of Rolling Stock Based on Fuzzy Logic Algorithms for Detecting Transport Movement Anomalies | journal = Intellectual Technologies on Transport | year = 2025 | issue = 3 | pages = 77-86 | doi = 10.20295/2413-2527-2025-343-77-86 }} EDN: NRDYHD

Copy

Export: BibTeX EndNote RefMan RusMARC

POSITIONING OF ROLLING STOCK BASED ON FUZZY LOGIC ALGORITHMS FOR DETECTING TRANSPORT MOVEMENT ANOMALIES Positioning of Rolling Stock Based on Fuzzy Logic Algorithms for Detecting Transport Movement Anomalies

Confirmation