Russian Federation
Russian Federation
Russian Federation
Russian Federation
UDK 007.52 не содержащие человека в качестве звена системы, роботы, автоматы
The features of modeling the movement of unmanned vehicles are investigated and approaches to modeling the flow of unmanned vehicles are being developed, taking into account promising solutions based on setting algorithms for vehicle behavior to ensure the target indicators of the transport process. Factors that take into account the features of unmanned vehicles modeling are proposed: priority for certain categories of vehicles, creation of separate parking lots, dynamic transfer from one route vehicle to another on the move, dynamic charging or refueling, dynamic cargo overload, multi-tiered vehicles, multi-link auto-driving without physical connection of individual links, high-speed movement of vehicles in conditions of high flow density. Also, using a specific example of the development of a macromodel of unmanned vehicles movement in conditions of high flow density, the features of it modeling movement are shown, consisting in the initial task of algorithms for the behavior of an autonomous vehicle, on the basis of which a model of traffic flow is already being built in the future.
unmanned vehicles, traffic flow modeling, oscillation theory, control theory, physical interpretation, transport network, digital twin, self-similar reduction, micromodel, macromodel
1. Bobrovskaya O. P. Problemy modelirovaniya smeshannogo transportnogo potoka / O. P. Bobrovskaya, T. V. Gavrilenko // Uspehi kibernetiki. 2023. T. 4, № 3. S. 39–46. DOI:https://doi.org/10.51790/2712-9942-2023-4-3-04. EDN ZQPFZJ.
2. Shamlickiy Ya. I. Modelirovanie transportnyh potokov sredstvami imitacionnogo modelirovaniya / Ya. I. Shamlickiy, E. V. Aleksandrov, E. I. Morozov // Estestvennye i tehnicheskie nauki. 2023. № 2 (177). S. 130–135. EDN ORPDBH.
3. L'vovich Ya. E. Problemy modelirovaniya transportnyh potokov / Ya. E. L'vovich, Yu. P. Preobrazhenskiy, E. Ruzhickiy // Vestnik Voronezhskogo instituta vysokih tehnologiy. 2022. № 3 (42). S. 72–74. EDN ARUBKQ
4. Gryaznov N. A. Obmen navigacionnoy informaciey dlya operativnogo upravleniya dorozhnym dvizheniem / N. A. Gryaznov // Informatika i avtomatizaciya. 2023. T. 22, № 1. S. 33–56. DOI:https://doi.org/10.15622/ia.22.1.2. EDN IEWXTP.
5. Zhang S., Hu X., Chen J., et al. An effective variational auto-encoder-based model for traffic flow imputation. Neural Comput & Applic. 36, 2617–2631 (2024). DOI:https://doi.org/10.1007/s00521–023–09127–2.
6. Li T. Mathematical Modelling of Traffic Flows. In: Hou, T. Y., Tadmor, E. (eds) Hyperbolic Problems: Theory, Numerics, Applications. Springer, Berlin, Heidelberg. 2003. DOI:https://doi.org/10.1007/978–3–642–55711–8_65.
7. Valuev A. M. Quasi-stationary Approach in Mathematical Modeling of Traffic Flows Dynamics in a City Road Network. In: Kozlov V., Buslaev A., Bugaev A., Yashina M., Schadschneider A., Schreckenberg M. (eds) Traffic and Granular Flow ‘11. Springer, Berlin, Heidelberg. 2013. DOI:https://doi.org/10.1007/978-3-642-39669-4_39.
8. Richter M., Paszkowski J. Modelling of traffic flow characteristics of traffic calmed roads. In: Brauweiler H. C., Kurchenkov V., Abilov S., Zirkler B. (eds) Digitalization and Industry 4.0: Economic and Societal Development. Springer Gabler, Wiesbaden. 2020. DOI:https://doi.org/10.1007/978–3–658–27110–7_17.
9. Chakroborty P., Maurya A. K. Modelling of Traffic Flow from an Engineer’s Perspective. In: Appert-Rolland C., Chevoir F., Gondret P., Lassarre S., Lebacque J. P., Schreckenberg M. (eds) Traffic and Granular Flow ’07. Springer, Berlin, Heidelberg. 2009. DOI:https://doi.org/10.1007/978- 3-540-77074-9_1.
10. Loktionova A. G. Opredelenie dinamicheskogo pokazatelya avtomobilya v transportnyh potokah gorodskoy transportnoy sistemy / A. G. Loktionova, A. G. Shevcova // Mir transporta i tehnologicheskih mashin. 2023. № 1–2 (80). S. 37–42. DOI:https://doi.org/10.33979/2073-7432-2023-2(80)-1-37-42. EDN AUSIDO.
11. Konovalova T. V. Nauchnye issledovaniya v oblasti modelirovaniya transportnyh potokov / T. V. Konovalova, S. L. Nadiryan, V. M. Plaksunova // Nauka. Tehnika. Tehnologii (politehnicheskiy vestnik). 2023. № 3. S. 33–36. EDN PZOYTB.
12. Scheglov V. I. Organizaciya i raspredelenie transportnyh potokov na osnove metodov matematicheskogo modelirovaniya / V. I. Scheglov // Inzhenernyy vestnik Dona. 2023. № 7 (103). S. 563–574. EDN OUZSMJ.
13. Liu H. Prediction Models of Traffic Flow Driven Based on Multi-Dimensional Data in Smart Traffic Systems. In: Smart Cities: Big Data Prediction Methods and Applications. Springer, Singapore. 2020. DOI:https://doi.org/10.1007/978- 981-15-2837-8_7.
14. Gribova V. V., Shamray N. B. & Fedorishchev L. A. Traffic modeling flows in a developing urban infrastructure with a software suite for creating interactive virtual environments. Autom Remote Control. 78, 235–246 (2017). DOI:https://doi.org/10.1134/S0005117917020047.
15. Ananenko A. O. Bespilotnye transportnye sredstva: problemy prakticheskogo ispol'zovaniya / A. O. Ananenko // Administrativnoe pravo i process. 2022. № 8. S. 71–74. DOI:https://doi.org/10.18572/2071-1166- 2022-8-71-74. EDN ZSGBTB.
16. Asankozhoev E. Zh. Gruzovye bespilotnye transportnye sredstva / E. Zh. Asankozhoev, A. N. Korkishko // Inzhenernyy vestnik Dona. 2022. № 11 (95). S. 755–761. EDN FXVCDL.
17. Moroz S. M. Algoritmy vyyavleniya otkazov i posleduyuschego protivoavariynogo upravleniya otkazavshim bespilotnym transportnym sredstvom S. M. Moroz // Avtomobil'naya promyshlennost'. 2023. № 8. S. 8–13. EDN RHHTKN.
18. Beklaryan A. L. Novaya programmnaya platforma dlya modelirovaniya transportnyh potokov s uchastiem bespilotnyh avtomobiley / A. L. Beklaryan // Vestnik CEMI. 2023. T. 6, № 1. DOI: 10.33276/ S265838870025116–0. EDN IDQZTT.
19. Małecki K., Kamiński M., Wąs J. A Multi-cell Cellular Automata Model of Traffic Flow with Emergency Vehicles: Effect of a Corridor of Life. In: Paszynski M., Kranzlmüller D., Krzhizhanovskaya V. V., Dongarra J. J., Sloot P. M. A. (eds) Computational Science — ICCS 2021. ICCS 2021. Lecture Notes in Computer Science. Vol. 12742. Springer, Cham. 2021. DOI:https://doi.org/10.1007/978-3-030-77961-0_4.
20. Baragunova L. A. Prodol'nye kolebaniya sterzhney ot dinamicheskih i kinematicheskih vozmuscheniy / L. A. Baragunova, M. M. Shogenova // Vestnik Dagestanskogo gosudarstvennogo tehnicheskogo universiteta. Tehnicheskie nauki. 2022. T. 49, № 2. S. 87–93. DOI:https://doi.org/10.21822/2073-6185-2022-49-2-87-93. EDN TFGQEK.
21. Tarlakovskiy D. V. Prodol'nye nestacionarnye kolebaniya konechnogo momentnogo uprugogo sterzhnya / D. V. Tarlakovskiy, G. V. Fedotenkov, Ch. May Kuok // Problemy prochnosti i plastichnosti. 2023. T. 85, № 3. S. 390–403. DOIhttps://doi.org/10.32326/1814-9146-2023-85-3-390-403. EDN PJJITC.
22. Smagin B. I. Reshenie zadachi Koshi dlya uravneniya kolebaniy struny / B. I. Smagin // Nauka i Obrazovanie. 2023. T. 6, № 2. EDN VQRBFM.
23. Orlyanskaya T. I. Ispol'zovanie sledstviy iz principa Dalambera dlya mehanicheskoy sistemy pri reshenii zadach dinamiki slozhnyh mehanicheskih sistem / T. I. Orlyanskaya // Tendencii razvitiya nauki i obrazovaniya. 2022. № 88–1. S. 24–28. DOI:https://doi.org/10.18411/trnio‑08–2022–06. EDN JWXMUK.
24. Wu Cx., Zhang P., Wong S. C., et al. Solitary wave solution to Aw-Rascle viscous model of traffic flow. Appl. Math. Mech.-Engl. Ed. 34, 523–528 (2013). DOI:https://doi.org/10.1007/s10483-013-1687-9.
25. Schmidt G. Shock waves of a continuous model of traffic flow. Computing. 9, 365–381 (1972). DOI:https://doi.org/10.1007/BF02241610.
26. Raphael E. Stern, Shumo Cui, Maria Laura Delle Monache, et. al. Work, Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments, Transportation Research Part C: Emerging Technologies. 2018. Vol. 89. P. 205–221, ISSN 0968–090X, DOI:https://doi.org/10.1016/j.trc.2018.02.005.
