Russian Federation
Russian Federation
Russian Federation
Russian Federation
UDK 007.52 не содержащие человека в качестве звена системы, роботы, автоматы
The paper proposes a new model of continuous traffic flow of unmanned autonomous cars when travelling on a serpentine with curves, taking into accountthe average speed of the cars ahead. The review of existing traffic flow models, in particular, the models of following the leader, such as the optimal speed model, generalised traffic model, model of total velocity difference is carried out. It is noted that these models do not take into account the peculiarities of traffic on serpentines with turns. On the basis of the existing models, a new model of continuous traffic flow has been developed, the principal difference of which is the consideration of the transverse slope of the road (turn) when travelling on a serpentine. The model takes into account the action of forces on the vehicle, including gravity, driving force and centripetal force. The prospects of using the developed model in the conditions of using intelligent transport systems, when the information about the average speed of vehicles moving ahead is transmitted over the network, are shown. Th proposed model allows to take into account the transverse slope of the road on curvilinear serpentine sections when calculating speed modes, to adequately assess the carrying capacity and identify potentially dangerous sections for optimisation of design decisions when creating unmanned transport.
unmanned vehicles, traffic flow modelling, serpentine, curve, serpentine traffic modelling, road, model, road safety
1. Transfer learning-based highway crash risk evaluation considering manifold characteristics of traffic flow / Q. Liu [et al.] // Accident Analysis & Prevention. 2022. Vol. 168. P. 106598. DOI:https://doi.org/10.1016/j. aap.2022.106598. EDN JZHZBQ.
2. VISSIM calibration and validation of urban traffic: a case study Al-Madinah City / M. A. R. Abdeen [et al.] // Personal and Ubiquitous Computing. 2023. Vol. 27, no. 5. P. 1747– 1756. DOI:https://doi.org/10.1007/s00779-023-01738-9. EDN WNQLET.
3. Bharathi D., Vanajakshi L., Subramanian Sh. C. Spatio-temporal modelling and prediction of bus travel time using a higher-order traffic flow model // Physica A: Statistical Mechanics and its Applications. 2022. Vol. 596. P. 127086. DOI:https://doi.org/10.1016/j.physa.2022.127086. EDN HXKVPV.
4. A Review on Atmospheric Dispersion System for Air Pollutants Integrated with GIS in Urban Environment // Nature Environment and Pollution Technology. 2022. Vol. 21, no. 4. P. 1553–1563. DOI:https://doi.org/10.46488/nept.2022.v21i04.008. EDN BDVMYZ.
5. End-to-End Machine Learning Pipeline for Real-Time Network Traffic Classification and Monitoring in Android Automotive / Sr. Muralidharan [et al.] // International Journal of Innovative Technology and Exploring Engineering. 2022. Vol. 11, no. 7. P. 32–38. DOI:https://doi.org/10.35940/ijitee. g9982.0611722. EDN BAIMAJ.
6. Moumen I., Abouchabaka Ja., Rafalia N. Adaptive traffic ights based on traffic flow prediction using machine learning models // International Journal of Electrical and Computer Engineering. 2023. Vol. 13, no. 5. P. 5813. DOI:https://doi.org/10.11591/ijece.v13i5.pp5813–5823. EDN QHJDOR.
7. Worst-case traffic assignment model for mixed traffic flow of human-driven vehicles and connected and autonomous vehicles by factoring in the uncertain link capacity / J. Wang [et al.] // Transportation Research Part C: Emerging Technologies. 2022. Vol. 140. P. 103703. DOI:https://doi.org/10.1016/j.trc.2022.103703. EDN LEHMDS.
8. Chen X., Wu Zh., Liang Yu. Modeling Mixed Traffic Flow with Connected Autonomous Vehicles and Human-Driven Vehicles in Off-Ramp Diverging Areas // Sustainability. 2023. Vol. 15, no. 7. P. 5651. DOI:https://doi.org/10.3390/su15075651. EDN SWOUPU.
9. Analysis and comparison of traffic flow models: a new hybrid traffic flow model vs benchmark models / F. Storani [et al.] // European Transport Research Review. 2021. Vol. 13, no. 1. DOI:https://doi.org/10.1186/s12544-021- 00515-0. EDN CADHKH.
10. Bilal M. T., Giglio D. Evaluation of macroscopic fundamental diagram characteristics for a quantified penetration rate of autonomous vehicles // European Transport Research Review. 2023. Vol. 15, no. 1. P. 10. DOI: 10.1186/ s12544-023-00579-0. EDN NQUMQS.
11. Modelirovanie odnopolosnogo transportnogo potoka bespilotnyh avtomobiley na osnove teorii sledovaniya za liderom / I. Yu. Kuverin [i dr.] // Avtomatika na transporte. 2024. T. 10, № 2. S. 166–177. DOI:https://doi.org/10.20295/2412-9186-2024-10-02-166-177. EDN FXSQXS.
12. Telematicheskaya sistema monitoringa dannyh avtomobilya / I. Yu. Kuverin [i dr.] // Nauchnaya zhizn'. 2023. T. 18, № 6 (132). S. 888–897. DOI:https://doi.org/10.26088/1991-9476-2023- 18-6-888-897. EDN RSOPZJ.
13. Gusev S. A., Kuverin I. Yu., Vasil'ev D. A. Napravleniya cifrovizacii transportnyh sistem v RF // Avtotransportnyy kompleks: strategiya, innovacii, kadry: sbornik nauchnyh trudov VIII Mezhdunarodnoy nauchno-prakticheskoy konferencii (Moskva, 24–25 marta 2022 goda). M.: Pero, 2022. S. 114–116. EDN SJLXCM.
14. Analysis and comparison of traffic flow models: a new hybrid traffic flow model vs benchmark models / F. Storani [et al.] // European Transport Research Review. 2021. Vol. 13, no. 1. DOI:https://doi.org/10.1186/s12544-021-00515-0. EDN CADHKH.
15. Pipes L. A. An operational analysis of traffic dynamics // Journal of Applied Physics. 1953. Vol. 24, iss. 3. P. 274–281. DOI:https://doi.org/10.1063/1.1721265.
16. Newell G. F. Nonlinear Effects in the Dynamics of Car Following // Operations Research. 1961. Vol. 9, no. 2. P. 209–229. DOI:https://doi.org/10.1287/opre.9.2.209.
17. Dynamical model of traffic congestion and numerical simulation / M. Bando [et al.] // Physical Review E. 1995. Vol. 51, iss. 2. P. 1035–1042. DOI:https://doi.org/10.1103/Phys- RevE.51.1035.
18. Komatsu T. S., Sasa S. -I. Dynamical model of traffic congestion and numerical simulation // Physical Review E. 1995. Vol. 51, iss. 2. P. 1035–1042. DOI:https://doi.org/10.1103/Phys- RevE.51.1035.
19. Lenz H., Wagner C. K., Sollacher R. Multi-anticipative carfollowing model // Eur. Phys. J. 1998. B. 7. P. 331–335. DOI:https://doi.org/10.1007/s100510050618.
20. Nagatani T. Stabilization and enhancement of traffic flow by the next-nearest-neighbor interaction // Physical Review E — Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 1999. Vol. 60, iss. 6. P. 6395–6401. DOI:https://doi.org/10.1103/PhysRevE.60.6395.
21. Sawada S. Nonlinear analysis of a differential-difference equation with next-nearest-neighbour interaction for traffic flow // Journal of Physics A: Mathematical and General. 2001. Vol. 34, iss. 50. P. 11253–11259. DOI:https://doi.org/10.1088/0305-4470/34/50/307.
22. Konishi K., Kokame H., Hirata K. Coupled map carfollowing model and its delayed-feedback control // Physical Review E — Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 1999. Vol. 60, iss. 4 A. P. 4000–4007. DOI:https://doi.org/10.1103/physreve.60.4000.
23. Konishi K., Kokame H., Hirata K. Decentralized delayed-feedback control of an optimal velocity traffic model // European Physical Journal B. 2000. Vol. 15, iss. 4. P. 715–722. DOI:https://doi.org/10.1007/s100510051176.
24. Zhao X., Gao Z. The stability analysis of the full velocity and acceleration velocity model // Physica A: Statistical Mechanics and its Applications, Elsevier. 2007. Vol. 375, no. 2. P. 679–686. DOI:https://doi.org/10.1016/j.physa.2006.10.03.
25. Phase transition on speed limit traffic with slope / X.-L. Li [et al.] // Chinese Physics B. 2008. Vol. 17, iss. 8. P. 3014– 3020. DOI:https://doi.org/10.1088/1674-1056/17/8/042.
26. Komada K., Masukura S., Nagatani T. Effect of gravitational force upon traffic flow with gradients // Physica A: Statistical Mechanics and its Applications. 2009. Vol. 388, iss. 14. P. 2880–2894. DOI:https://doi.org/10.1016/j.physa.2009.03.029.
27. Zhu W.-X., Yu R.-L. Nonlinear analysis of traffic flow on a gradient highway // Physica A: Statistical Mechanics and its Applications. 2012. Vol. 391, iss. 4. P. 954–965. DOIhttps://doi.org/10.1016/j.physa.2011.09.026.
28. Liang Y.-J., Xue Y. Study on traffic flow affected by the road turning Wuli Xuebao // Acta Physica Sinica. 2010. Vol. 59, iss. 8. P. 5325–5331.
29. Zhu W.-X., Zhang L.-D. Friction coefficient and radius of curvature effects upon traffic flow on a curved Road // Physica A: Statistical Mechanics and its Applications. 2012. Vol. 391, iss. 20. P. 4597–4605. DOI:https://doi.org/10.1016/j.physa. 2012.05.032.
30. Zhu W.-X., Yu R.-L. A new car-following model considering the related factors of a gyroidal road // Physica A: Statistical Mechanics and its Applications. 2014. Vol. 393. P. 101–111. DOI:https://doi.org/10.1016/j.physa.2013.09.049.
31. Zhai C., Wu W., Xiao Y. 2023. Modeling continuous traffic flow with the average velocity effect of multiple vehicles ahead on gyroidal roads // Digital Transportation and Safety. 2023. Vol. 2, iss. 2. P. 124–138. DOI: 10.48130/ DTS‑2023–0010.
32. Minenko E. Yu., Kusmorova E. Yu. Povyshenie bezopasnosti dorozhnogo dvizheniya na virazhe // Mir transporta i tehnologicheskih mashin. 2015. № 1 (48). S. 103–110. EDN TNIJJL.
33. Mihaylov K. A. Proektirovanie virazha // Teoreticheskie i prakticheskie aspekty razvitiya sovremennoy nauki: teoriya, metodologiya, praktika: sbornik nauchnyh statey po materialam IX Mezhdu- narodnoy nauchno-prakticheskoy konferencii (Ufa, 29 noyabrya 2022 goda). Ch. 2. Ufa: NIC «Vestnik nauki», 2022. S. 219–224. EDN YGYAQS.
34. Tarasik V. P. Ocenka upravlyaemosti i ustoychivo- sti avtomobilya pri dvizhenii na virazhe // Gruzovik. 2020. № 11. S. 22–29. EDN EDPLNH.
35. Mustafin A. F. Analiz metodik rascheta virazha po otechestvennym i zarubezhnym normativnym dokumentam dlya avtomobil'noy dorogi IV kategorii // Perspektivnye nauchnye issledovaniya: opyt, problemy i perspektivy razvitiya: sbornik nauchnyh statey po materialam VI Mezhdunarodnoy nauchno-prakticheskoy konferencii (Ufa, 26 noyabrya 2021 goda). Ufa: NIC «Vestnik nauki», 2021. S. 52–56. EDN MRKKTJ.
36. Minenko E. Yu., Kusmorova Yu. A. Povyshenie bez- opasnosti dorozhnogo dvizheniya na virazhe // Mir transporta i tehnologicheskih mashin. 2015. № 1 (48). S. 103–110. EDN TNIJJL.
37. Dynamical model of traffic congestion and numerical simulation / M. Bando [et al.] // Physical Review E. 1995. Vol. 51. P. 1035–1042 DOI:https://doi.org/10.1103/physreve.51.1035.
38. Helbing D., Tilch B. Generalized force model of traffic dynamics // Physical Review E. 1998. Vol. 58. P. 133–138. DOI:https://doi.org/10.1103/physreve.58.133.
39. Jiang R., Wu Q., Zhu Z. Full velocity difference model for a car-following theory // Physical Review E. 2001. Vol. 64. P. 017101. DOI:https://doi.org/10.1103/PhysRevE.64.017101.
40. Zhu W., Yu R. A new car-following model considering the related factors of a gyroidal road // Physica A: Statistical Mechanics and Its Applications. 2014. Vol. 393. P. 101– 111. DOI:https://doi.org/10.1016/j.physa.2013.09.049.
41. Sun D., Kang Y., Yang S. A novel car following model considering average speed of preceding vehicles group // Physica A: Statistical Mechanics and Its Applications. 2015. Vol. 436. P. 103–109. DOI:https://doi.org/10.1016/j.physa.2015.04.028.
42. Multi-anticipative average flux effect in the lattice hydrodynamic model / H. Kuang [et al.] // IEEE Access. 2021. Vol. 9. P. 35279–35286. DOI:https://doi.org/10.1109/access. 2021.3060080.
43. Zhai C., Wu W., Xiao Y. Modeling continuous traffic flow with the average velocity effect of multiple vehicles ahead on gyroidal roads // Digital Transportation and Safety. 2023. Vol. 2, iss. 2. P. 124–138. DOI: 10.48130/ DTS‑2023-0010.
44. Malicious traffic detection on sampled network flow data with novelty-detection- based models / A. Campazas-Vega [et al.] // Scientific Reports. 2023. Vol. 13, no. 1. P. 15446. DOI:https://doi.org/10.1038/s41598-023-42618-9. EDN EYVFBM.
45. Traffic flow prediction under multiple adverse weather based on self-attention mechanism and deep learning models / W. Zhang [et al.] // Physica A: Statistical Mechanics and its Applications. 2023. Vol. 625. P. 128988. DOI:https://doi.org/10.1016/j.physa.2023.128988. EDN OTBNLX.
46. Vorob'ev A. I. Upravlenie dvizheniem vysokoavtomatizirovannyh avtotransportnyh sredstv v cifrovoy modeli dorozhnogo dvizheniya // XIV Vserossiyskaya mul'tikonferenciya po problemam upravleniya MKPU‑2021: materialy XIV Mul'tikonferencii (Divnomorskoe, Gelendzhik, 27 sentyabrya – 2 oktyabrya 2021 goda): v 4 t. Rostov-na-Donu; Taganrog: Yuzhnyy federal'nyy universitet, 2021. T. 4. S. 145–147. EDN QJZKXM.