Affiliation:
1. Russia University of Transport (MIIT)
2. Russian University of Transport (MIIT)
Abstract
The article analyses the technological process of transportation process organization and its control in different systems of urban agglomeration rapid-transit transport. It is presented the generalization of organization technological schemes of transportation process at the compilation of normative documents-schedules of: train traffic, rolling stock turnover, work of locomotive teams. Common features, allowing to share automation and digitization positive experience from one transport systems to another, are revealed. As a typical example for urban agglomeration rapid-transit transport, the work of Moscow Central Ring in data flow diagram notation is considered. It has been shown that the conditions of traffic planning on Moscow Central Ring are analogous to active ones on the subway ring lines. As generalizing notions, there are: mixing, non-parallelism, zoning, non-autonomy. Corresponding illustrative examples are given. Congested experience in the sphere of control automation for transportation process of rapid-transit transport of urban agglomeration is considered on the examples of railway section Nizhniy Novgorod – Uren’ as well as Kaluzhsko-Rizhskaya line of Moscow subway (electrodepots “Kaluzhskoye” and “Sviblovo”). The article describes initial data sets for to perform train traffic schedule, the purpose of its performance has been formulated, limitations, reflecting the links between objects, inside the set of given resources, and limitations, being defined by rules of passenger service, have been revealed. Analysis, pursued in the article, has shown the perspective directions of automated transport systems development on knowledge accumulated bases. As a result of the application of complex approach to the solution of automated control tasks at the use of artificial intelligence technologies and big databases usage, it’s planned to increase the efficiency usage for given resources set, train traffic schedule implementation percentage and others; to reduce information transfer error number as well as those, appeared as a result of negative human factor influence and so on.
Publisher
Petersburg State Transport University
Reference57 articles.
1. Баранов Л. А. Комплексное решение задач планирования и управления движением городских рельсовых транспортных средств / Л. А. Баранов, В. Г. Сидоренко, Е. П. Балакина и др. // Академик Владимир Николаевич Образцов — основоположник транспортной науки: труды Международной научно-практической конференции, посвященной 125-летию университета, Москва, 22 октября 2021 года. — М.: РУТ (МИИТ), 2021. — С. 56–64., Baranov L. A. Kompleksnoe reshenie zadach planirovaniya i upravleniya dvizheniem gorodskih rel'sovyh transportnyh sredstv / L. A. Baranov, V. G. Sidorenko, E. P. Balakina i dr. // Akademik Vladimir Nikolaevich Obrazcov — osnovopolozhnik transportnoy nauki: trudy Mezhdunarodnoy nauchno-prakticheskoy konferencii, posvyaschennoy 125-letiyu universiteta, Moskva, 22 oktyabrya 2021 goda. — M.: RUT (MIIT), 2021. — S. 56–64.
2. Вакуленко С. П. Разработка вариантов модернизации Московской монорельсовой транспортной системы / С. П. Вакуленко, Д. Ю. Роменский, В. А. Мнацаканов и др. // Метро и тоннели. — 2020. — № 4. — С. 28–36., Vakulenko S. P. Razrabotka variantov modernizacii Moskovskoy monorel'sovoy transportnoy sistemy / S. P. Vakulenko, D. Yu. Romenskiy, V. A. Mnacakanov i dr. // Metro i tonneli. — 2020. — № 4. — S. 28–36.
3. Shevlyugin M. V. Electric stock digital twin in a subway traction power system / M. V. Shevlyugin, A. A. Korolev, A. E. Golitsyna et al. // Russian Electrical Engineering. — 2019. — Vol. 90. — Iss. 9. — Pp. 647–652. — DOI: 10.3103/S1068371219090098., Shevlyugin M. V. Electric stock digital twin in a subway traction power system / M. V. Shevlyugin, A. A. Korolev, A. E. Golitsyna et al. // Russian Electrical Engineering. — 2019. — Vol. 90. — Iss. 9. — Pp. 647–652. — DOI: 10.3103/S1068371219090098.
4. Zhou W. Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine / W. Zhou, W. Wang, D. Zhao // Sensors. — 2020. — Vol. 20. — Iss. 12. — Pp. 1–23. — DOI: 10.3390/s20123555., Zhou W. Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine / W. Zhou, W. Wang, D. Zhao // Sensors. — 2020. — Vol. 20. — Iss. 12. — Pp. 1–23. — DOI: 10.3390/s20123555.
5. Пазойский Ю. О. Выбор оптимальных параметров системы освоения пассажиропотоков в дальнем сообщении на железнодорожном транспорте / Ю. О. Пазойский, О. Н. Панова // Автоматизация и современные технологии. — 2008. — № 1. — С. 34–39., Pazoyskiy Yu. O. Vybor optimal'nyh parametrov sistemy osvoeniya passazhiropotokov v dal'nem soobschenii na zheleznodorozhnom transporte / Yu. O. Pazoyskiy, O. N. Panova // Avtomatizaciya i sovremennye tehnologii. — 2008. — № 1. — S. 34–39.