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Feedforward feedback iterative learning control method for the multilayer boundaries of oversaturated intersections based on the macroscopic fundamental diagram.

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The feedback control based on the model and method of iterative learning control, which in turn is based on the macroscopic fundamental diagram (MFD), mostly belongs to the classification of single-layer boundary control method. However, the feedback control method has the problem of time delay. Therefore, a feed forward feedback iterative learning control (FFILC) method based on MFD of the multi-layer boundary of single-area oversaturated intersections is proposed. The FFILC method can improve the effectiveness of boundary control and avoid the time-delay problem of feedback control. Firstly, MFD theory is used to determine the MFD of the control area; the congestion zone and the transition zone of the control area are identified; and the two-layer boundary of the control area is determined. Then, the FFILC controllers are established at the two-layer boundary of the control area. When the control area enters into a congestion state, the control ratio of traffic flow in and out of the two-layer boundary is adjusted. The cumulative number of vehicles in the control area continues to approach the optimal cumulative number of vehicles, and it maintains high traffic efficiency with high flow rates. Finally, The actual road network is taken as the experimental area, and the road network simulation platform is built. The controller of the feedforward iterative learning control (FILC) is selected as the comparative controller and used to analyse the iterative effect of FFILC. Improvements in the use of traffic signal control indicators for the control area are analysed after the implementation of the FFILC method. Results show that the FFILC method considerably reduces the number of iterations, and it can effectively improve convergence speed and the use of traffic signal evaluation indicators for the control area.
Rocznik
Strony
67--87
Opis fizyczny
Bibliogr. 30 poz., rys., tab., wykr.
Twórcy
autor
  • Institute of Rail Traffic, Guangdong Communication Polytechnic, Guangzhou, China
autor
  • School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
Bibliografia
  • [1] ARIMOTO, S., KAWAMURA, S., & MIYAZAKI, F., 1984. Bettering operation of robots by learning. Journal of Robotic Systems, 1(2), 123-140.
  • [2] CHI, R.H., C., LI, J. Y., LIU, X. P., & SUI, S. L., 2013. Iterative learning control for freeway traffic distributed parameter systems. Journal of Transportation Systems Engineering and In-formation Technology,13(02):42-47.
  • [3] DAGANZO, C. F., 2007. Urban gridlock: macroscopic modeling and mitigation approaches. Transportation Research, Part B (Methodological), 41(1), 49-62.
  • [4] DAGANZO, C. F., GAYAH, V.V., & GONZALES, E. J., 2011. Macroscopic relations of urban traffic variables: bifurcations, multivaluedness and instability. Transportation Research Part B: Methodological, 45(1), 278-288.
  • [5] GAYAH, V. V., & DAGANZO, C. F., 2011. Clockwise hysteresis loops in the macroscopic fundamental diagram: an effect of network instability. Transportation Research Part B, 45(4), 643-655.
  • [6] GEROLIMINIS, N., & DAGANZO, C. F., 2008. Existence of urban-scale macroscopic fundamental diagrams: some experimental findings. Transportation Research Part B: Methodological, 42(9), 759-770.
  • [7] GEROLIMINIS, N., & SUN, J., 2011. Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transportation Research Part B Methodological, 45(3), 605-617.
  • [8] GEROLIMINIS, N., HADDAD, J., & RAMEZANI, M., 2013. Optimal perimeter control for two urban regions with macroscopic fundamental diagrams: a model predictive approach. IEEE Transactions on Intelligent Transportation Systems, 14(1), 348-359.
  • [9] GODFREY, J. W., 1969. The mechanism of a road network. Traffic Engineering & Control, 11(7), 323-327.
  • [10] GONZALES, E. J., CHAVIS, C.,& LI, Y. W., ET AL., 2009. Multimodal transport modeling for Nairobi, Kenya: insights and recommendations with an evidence-based model. Institute of Transportation Studies, UC Berkeley.
  • [11] HADDAD, J., & MIRKIN, B., 2016. Adaptive perimeter traffic control of urban road networks based on mfd model with time delays. International Journal of Robust and Nonlinear Control, 26(6), 1267-1285.
  • [12] HADDAD, J., & SHRAIBER, A., 2014. Robust perimeter control design for an urban region. Transportation Research Part B: Methodological, 68, 315-332.
  • [13] HADDAD, J., RAMEZANI, M., & GEROLIMINIS, N., 2012. Model predictive perimeter control for urban areas with macroscopic fundamental diagrams. American Control Conference (ACC) IEEE, 5757-5762.
  • [14] HAJIAHMADI, M., HADDAD, J., DE SCHUTTER, B., & GEROLIMINIS, N., 2015. Optimal hybrid perimeter and switching plans control for urban traffic networks. IEEE Transactions on Control Systems Technology, 23(2), 464-478.
  • [15] HOU, Z., XU, J. X., & ZHONG, H.,2007. Freeway traffic control using iterative learning control-based ramp metering and speed signaling. IEEE Transactions on Vehicular Technology, 56(2), 466-477.
  • [16] JIN,S. T., DING, Y., YIN,C. K., & HOU, Z. S., 2018. Iterative learning perimeter control for urban traffic region. Control and Decision, 33(04), 633-638.
  • [17] KEYVAN-EKBATANI, M., PAPAGEORGIOU, M., & PAPAMICHAIL, I., 2013. Urban congestion gating control based on reduced operational network fundamental diagrams. Transportation Research Part C Emerging Technologies, 33, 74-87.
  • [18] KEYVAN-EKBATANI, M., YILDIRI-MOGLU, M., GEROLIMINIS, N., & PAPAGEORGIOU, M., 2015. Multiple concentric gating traffic control in large-scale urban networks. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2141-2154.
  • [19] KLOS, M.J., SOBOTA, A., 2019. Performance evaluation of roundabouts using a microscopic simulation model. Scientific Journal of Silesian University of Technology. Series Trans-port,104: 57-67.
  • [20] LIN, X. H., XU, J. M., & LIN, P.Q., ET AL., 2017. Improved road-network-flow control strategy based on macroscopic fundamental diagrams and queuing length in connected-vehicle network. Mathematical Problems in Engineering, 2017(1), 1-7.
  • [21] LIN, X. H., XU, J. M., & Zhou, W. J., 2019.A Dynamic partitioning method for the multilayer boundary for urban oversaturated homogeneous traffic networks based on macroscopic fundamental diagrams in connected-vehicle network. Advances in Transportation Studies, 48(7):47-62.
  • [22] LIN, X. H., XU, J. M., CAO, C.T., 2019. Simulation and comparison of two fusion methods for macroscopic fundamental diagram estimation. Archives of Transport, 51(3) ,35-48.
  • [23] NAGLE, A. S., & GAYAH, V. V., 2014. Accuracy of networkwide traffic states estimated from mobile probe data. Transportation Research Record Journal of the Transportation Research Board, 2421(2421), 1-11.
  • [24] RAMEZANI, M., HADDAD, J., & GEROLI-MINIS, N., 2012. Macroscopic traffic control of a mixed urban and freeway network. IFAC Proceedings Volumes, 45(24), 89-94.
  • [25] SHANG, Q., LIN, C. Y., YAO, Z. S., &BING,Q. C., et al., 2017. Traffic state identification for urban expressway based on spectral clustering and rs-knn. Journal of South China University of Technology, 45(6), 52-58.
  • [26] UCHIYAMA, M., 1978. Formation of high-speed motion pattern of a mechanical arm by trial. transactions of the society of instrument & control engineers, 14(6), 706-712.
  • [27] YAN, F., 2016. Research on iterative learning control methods for urban traffic signals. Northwestern Polytechnical University.
  • [28] YAN, F., TIAN, F. L., & SHI, Z. K., 2016. Robust iterative learning control for signals at urban road intersections. China Journal of Highway and Transport, 29(01), 120-127.
  • [29] YAN, F., TIAN, F., & SHI, Z.,2016. Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks. International Journal of Modern Physics C, 27(04), 10-20.
  • [30] ZHAO, H., HE, R., SU, J., 2018. Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections. Archives of Transport, 46(2), 85-96.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-33d74798-58ef-4afa-865e-8bf2e79586a1
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