An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Published Online:https://doi.org/10.1287/trsc.2023.0091

References

  • Aboudolas K, Geroliminis N (2013) Perimeter and boundary flow control in multi-reservoir heterogeneous networks. Transportation Res. Part B Methodological 55:265–281.CrossrefGoogle Scholar
  • Ambühl L, Menendez M (2016) Data fusion algorithm for macroscopic fundamental diagram estimation. Transportation Res. Part C Emerging Tech. 71:184–197.CrossrefGoogle Scholar
  • Ambühl L, Loder A, Bliemer MC, Menendez M, Axhausen KW (2018) Introducing a re-sampling methodology for the estimation of empirical macroscopic fundamental diagrams. Transportation Res. Rec. 2672(20):239–248.CrossrefGoogle Scholar
  • Ameli M, Faradonbeh MSS, Lebacque JP, Abouee-Mehrizi H, Leclercq L (2022) Departure time choice models in urban transportation systems based on mean field games. Transport. Sci. 56(6):1483–1504.LinkGoogle Scholar
  • Ampountolas K, Zheng N, Geroliminis N (2017) Macroscopic modelling and robust control of bi-modal multi-region urban road networks. Transportation Res. Part B Methodological 104:616–637.CrossrefGoogle Scholar
  • Baldi S, Michailidis I, Ntampasi V, Kosmatopoulos E, Papamichail I, Papageorgiou M (2019) A simulation-based traffic signal control for congested urban traffic networks. Transportation Sci. 53(1):6–20.LinkGoogle Scholar
  • Batista SF, Leclercq L (2019) Regional dynamic traffic assignment framework for macroscopic fundamental diagram multi-regions models. Transportation Sci. 53(6):1563–1590.LinkGoogle Scholar
  • Batista S, Leclercq L, Geroliminis N (2019) Estimation of regional trip length distributions for the calibration of the aggregated network traffic models. Transportation Res. Part B Methodological 122:192–217.CrossrefGoogle Scholar
  • Batista SF, Ingole D, Leclercq L, Menéndez M (2021) The role of trip lengths calibration in model-based perimeter control strategies. IEEE Trans. Intelligent Transportation Systems 23(6):5176–5186.CrossrefGoogle Scholar
  • Buisson C, Ladier C (2009) Exploring the impact of homogeneity of traffic measurements on the existence of macroscopic fundamental diagrams. Transportation Res. Rec. 2124(1):127–136.CrossrefGoogle Scholar
  • Cao J, Menendez M (2015) System dynamics of urban traffic based on its parking-related-states. Transportation Res. Part B Methodological 81:718–736.CrossrefGoogle Scholar
  • Chen C, Huang Y, Lam W, Pan T, Hsu S, Sumalee A, Zhong R (2022) Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics. Transportation Res. Part C Emerging Tech. 142:103759.CrossrefGoogle Scholar
  • Daganzo CF (2007) Urban gridlock: Macroscopic modeling and mitigation approaches. Transportation Res. Part B Methodological 41(1):49–62.CrossrefGoogle Scholar
  • Ding H, Di Y, Feng Z, Zhang W, Zheng X, Yang T (2022) A perimeter control method for a congested urban road network with dynamic and variable ranges. Transportation Res. Part B Methodological 155:160–187.CrossrefGoogle Scholar
  • Fu H, Liu N, Hu G (2017) Hierarchical perimeter control with guaranteed stability for dynamically coupled heterogeneous urban traffic. Transportation Res. Part C Emerging. Tech. 83:18–38.CrossrefGoogle Scholar
  • Fu H, Chen S, Chen K, Kouvelas A, Geroliminis N (2021) Perimeter control and route guidance of multi-region MFD systems with boundary queues using colored petri nets. IEEE Trans. Intelligent Transportation Systems 23(8):12977–12999.Google Scholar
  • Gao S, Li D, Zheng N, Hu R, She Z (2022) Resilient perimeter control for hyper-congested two-region networks with MFD dynamics. Transportation Res. Part B Methodological 156:50–75.CrossrefGoogle Scholar
  • Geroliminis N, Boyaci B (2012) The effect of variability of urban systems characteristics in the network capacity. Transportation Res. Part B Methodological 46(10):1607–1623.CrossrefGoogle Scholar
  • Geroliminis N, Daganzo CF (2008) Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transportation Res. Part B Methodological 42(9):759–770.CrossrefGoogle Scholar
  • Geroliminis N, Sun J (2011) Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transportation Res. Part B Methodological 45(3):605–617.CrossrefGoogle Scholar
  • Geroliminis N, Haddad J, Ramezani M (2013) Optimal perimeter control for two urban regions with macroscopic fundamental diagrams: A model predictive approach. IEEE Trans. Intelligent Transportation Systems 14(1):348–359.CrossrefGoogle Scholar
  • Godfrey J (1969) The mechanism of a road network. Traffic Engrg. Control 11:323–327.Google Scholar
  • Gu Z, Shafiei S, Liu Z, Saberi M (2018) Optimal distance-and time-dependent area-based pricing with the network fundamental diagram. Transporation Res. Part C Emerging Tech. 95:1–28.CrossrefGoogle Scholar
  • Haddad J (2015) Robust constrained control of uncertain macroscopic fundamental diagram networks. Transportation Res. Part C Emerging Tech. 59:323–339.CrossrefGoogle Scholar
  • Haddad J, Mirkin B (2020) Resilient perimeter control of macroscopic fundamental diagram networks under cyberattacks. Transportation Res. Part B Methodological 132:44–59.CrossrefGoogle Scholar
  • Haddad J, Shraiber A (2014) Robust perimeter control design for an urban region. Transportation Res. Part B Methodological 68:315–332.CrossrefGoogle Scholar
  • Haddad J, Ramezani M, Geroliminis N (2013) Cooperative traffic control of a mixed network with two urban regions and a freeway. Transportation Res. Part B Methodological 54:17–36.CrossrefGoogle Scholar
  • Hamedmoghadam H, Zheng N, Li D, Vu HL (2022) Percolation-based dynamic perimeter control for mitigating congestion propagation in urban road networks. Transportation Res. Part C Emerging Tech. 145:103922.CrossrefGoogle Scholar
  • Hou Z, Lei T (2020) Constrained model free adaptive predictive perimeter control and route guidance for multi-region urban traffic systems. IEEE Trans. Intelligent Transportation Systems 23(2):912–924.CrossrefGoogle Scholar
  • Hu Z, Ma W (2024) Demonstration-guided deep reinforcement learning for coordinated ramp metering and perimeter control in large scale networks. Transportation Res. Part C Emerging Tech. 159:104461.CrossrefGoogle Scholar
  • Huang Y, Xiong J, Sumalee A, Zheng N, Lam W, He Z, Zhong R (2020) A dynamic user equilibrium model for multi-region macroscopic fundamental diagram systems with time-varying delays. Transportation Res. Part B Methodological 131:1–25.CrossrefGoogle Scholar
  • Jiang S, Tran CQ, Keyvan-Ekbatani M (2024) Regional route guidance with realistic compliance patterns: Application of deep reinforcement learning and MPC. Transportation Res. Part C Emerging Tech. 158:104440.CrossrefGoogle Scholar
  • Keyvan-Ekbatani M, Papageorgiou M, Knoop VL (2015) Controller design for gating traffic control in presence of time-delay in urban road networks. Transportation Res. Proc. 7:651–668.CrossrefGoogle Scholar
  • Keyvan-Ekbatani M, Kouvelas A, Papamichail I, Papageorgiou M (2012) Exploiting the fundamental diagram of urban networks for feedback-based gating. Transportation Res. Part B Methodological 46(10):1393–1403.CrossrefGoogle Scholar
  • Knoop V, Hoogendoorn S, Van Lint J (2012) Routing strategies based on macroscopic fundamental diagram. Transportation Res. Rec. 2315(1):1–10.CrossrefGoogle Scholar
  • Kouvelas A, Saeedmanesh M, Geroliminis N (2017) Enhancing model-based feedback perimeter control with data-driven online adaptive optimization. Transportation Res. Part B Methodological 96:26–45.CrossrefGoogle Scholar
  • Kouvelas A, Saeedmanesh M, Geroliminis N (2023) A linear-parameter-varying formulation for model predictive perimeter control in multi-region MFD urban networks. Transportation Sci. 57(6):1496–1515.LinkGoogle Scholar
  • Kumarage S, Yildirimoglu M, Ramezani M, Zheng Z (2021) Schedule-constrained demand management in two-region urban networks. Transportation Sci. 55(4):857–882.LinkGoogle Scholar
  • Leclercq L, Geroliminis N (2013) Estimating MFDs in simple networks with route choice. Transportation Res. Part B Methodological 57:468–484.CrossrefGoogle Scholar
  • Leclercq L, Sénécat A, Mariotte G (2017) Dynamic macroscopic simulation of on-street parking search: A trip-based approach. Transportation Res. Part B Methodological 101:268–282.CrossrefGoogle Scholar
  • Lee J, Sutton RS (2021) Policy iterations for reinforcement learning problems in continuous time and space: Fundamental theory and methods. Automatica J. IFAC 126:109421.CrossrefGoogle Scholar
  • Lei T, Hou Z, Ren Y (2019) Data-driven model free adaptive perimeter control for multi-region urban traffic networks with route choice. IEEE Trans. Intelligent Transportation Systems 21(7):2894–2905.Google Scholar
  • Li Y, Mohajerpoor R, Ramezani M (2021) Perimeter control with real-time location-varying cordon. Transportation Res. Part B Methodological 150:101–120.CrossrefGoogle Scholar
  • Mahmassani HS, Williams JC, Herman R (1984) Investigation of network-level traffic flow relationships: Some simulation results. Transportation Res. Rec. 971:121–130.Google Scholar
  • Mariotte G, Leclercq L, Batista S, Krug J, Paipuri M (2020) Calibration and validation of multi-reservoir MFD models: A case study in Lyon. Transportation Res. Part B Methodological 136:62–86.CrossrefGoogle Scholar
  • Mercader P, Haddad J (2021) Resilient multivariable perimeter control of urban road networks under cyberattacks. Control Engrg. Practice 109:104718.CrossrefGoogle Scholar
  • Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, et al. (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529.CrossrefGoogle Scholar
  • Modares H, Lewis FL, Naghibi-Sistani MB (2014) Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems. Automatica J. IFAC 50:193–202.CrossrefGoogle Scholar
  • Mohajerpoor R, Saberi M, Vu HL, Garoni TM, Ramezani M (2020) H ∞ robust perimeter flow control in urban networks with partial information feedback. Transportation Res. Part B Methodological 137:47–73.CrossrefGoogle Scholar
  • Moshahedi N, Kattan L (2023) Alpha-fair large-scale urban network control: A perimeter control based on a macroscopic fundamental diagram. Transportation Res. Part C Emerging Tech. 146:103961.CrossrefGoogle Scholar
  • Murray JJ, Cox CJ, Lendaris GG, Saeks R (2002) Adaptive dynamic programming. IEEE Trans. Systems Man Cybernics C 32(2):140–153.CrossrefGoogle Scholar
  • Ogata K (2010) Modern Control Engineering, vol. 5 (Prentice Hall, Upper Saddle River, NJ).Google Scholar
  • Paipuri M, Barmpounakis E, Geroliminis N, Leclercq L (2021) Empirical observations of multi-modal network-level models: Insights from the pneuma experiment. Transportation Res. Part C Emerging Tech. 131:103300.CrossrefGoogle Scholar
  • Ramezani M, Valadkhani AH (2023) Dynamic ride-sourcing systems for city-scale networks-part I: Matching design and model formulation and validation. Transportation Res. Part C Emerging Tech. 152:104158.CrossrefGoogle Scholar
  • Ramezani M, Haddad J, Geroliminis N (2015) Dynamics of heterogeneity in urban networks: Aggregated traffic modeling and hierarchical control. Transportation Res. Part B Methodological 74:1–19.CrossrefGoogle Scholar
  • Ren Y, Hou Z, Sirmatel II, Geroliminis N (2020) Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks. Transportation Res. Part C Emerging Tech. 115:102618.CrossrefGoogle Scholar
  • Saberi M, Mahmassani HS (2013) Hysteresis and capacity drop phenomena in freeway networks: Empirical characterization and interpretation. Transportation Res. Rec. 2391(1):44–55.CrossrefGoogle Scholar
  • Saeedmanesh M, Geroliminis N (2016) Clustering of heterogeneous networks with directional flows based on “snake” similarities. Transportation Res. Part B Methodological 91:250–269.CrossrefGoogle Scholar
  • Saeedmanesh M, Geroliminis N (2017) Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks. Transportation Res. Proc. 23:962–979.CrossrefGoogle Scholar
  • Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, et al. (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484.CrossrefGoogle Scholar
  • Sirmatel II, Geroliminis N (2018) Economic model predictive control of large-scale urban road networks via perimeter control and regional route guidance. IEEE Trans. Intelligent Transportation Systems 19(4):1112–1121.CrossrefGoogle Scholar
  • Sirmatel II, Geroliminis N (2019) Nonlinear moving horizon estimation for large-scale urban road networks. IEEE Trans. Intelligent Transportation Systems 21(12):4983–4994.CrossrefGoogle Scholar
  • Sirmatel II, Tsitsokas D, Kouvelas A, Geroliminis N (2021) Modeling, estimation, and control in large-scale urban road networks with remaining travel distance dynamics. Transportation Res. Part C Emerging Tech. 128:103157.CrossrefGoogle Scholar
  • Su Z, Chow AH, Fang C, Liang E, Zhong R (2023) Hierarchical control for stochastic network traffic with reinforcement learning. Transportation Res. Part B Methodological 167:196–216.CrossrefGoogle Scholar
  • Su Z, Chow AH, Zheng N, Huang Y, Liang E, Zhong R (2020) Neuro-dynamic programming for optimal control of macroscopic fundamental diagram systems. Transportation Res. Part C Emerging Tech. 116:102628.CrossrefGoogle Scholar
  • Sutton RS, Barto AG (2018) Reinforcement Learning: An Introduction (MIT Press, Cambridge, MA).Google Scholar
  • Tsitsokas D, Kouvelas A, Geroliminis N (2023) Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient max pressure with perimeter control. Transportation Res. Part C Emerging Tech. 152:104128.CrossrefGoogle Scholar
  • Tsubota T, Bhaskar A, Chung E (2014) Macroscopic fundamental diagram for Brisbane, Australia: Empirical findings on network partitioning and incident detection. Transportation Res. Rec. 2421(1):12–21.CrossrefGoogle Scholar
  • Valadkhani AH, Ramezani M (2023) Dynamic ride-sourcing systems for city-scale networks, part II: Proactive vehicle repositioning. Transportation Res. Part C Emerging Tech. 152:104159.CrossrefGoogle Scholar
  • Wei B, Saberi M, Zhang F, Liu W, Waller ST (2020) Modeling and managing ridesharing in a multi-modal network with an aggregate traffic representation: A doubly dynamical approach. Transportation Res. Part C Emerging Tech. 117:102670.CrossrefGoogle Scholar
  • Yildirimoglu M, Geroliminis N (2014) Approximating dynamic equilibrium conditions with macroscopic fundamental diagrams. Transportation Res. Part B Methodological 70:186–200.CrossrefGoogle Scholar
  • Yildirimoglu M, Ramezani M (2020) Demand management with limited cooperation among travellers: A doubly dynamic approach. Transportation Res. Part B Methodological 132:267–284.CrossrefGoogle Scholar
  • Yildirimoglu M, Ramezani M, Geroliminis N (2015) Equilibrium analysis and route guidance in large-scale networks with MFD dynamics. Transportation Res. Proc. 9:185–204.CrossrefGoogle Scholar
  • Yildirimoglu M, Sirmatel II, Geroliminis N (2018) Hierarchical control of heterogeneous large-scale urban road networks via path assignment and regional route guidance. Transportation Res. Part B Methodological 118:106–123.CrossrefGoogle Scholar
  • Zheng N, Geroliminis N (2020) Area-based equitable pricing strategies for multimodal urban networks with heterogeneous users. Transportation Res. Part A Policy Practice 136:357–374.CrossrefGoogle Scholar
  • Zhong R, Sumalee A, Pan T, Lam W (2014) Optimal and robust strategies for freeway traffic management under demand and supply uncertainties: An overview and general theory. Transportmetrica A Transportation Sci. 10(10):849–877.CrossrefGoogle Scholar
  • Zhong R, Chen C, Huang Y, Sumalee A, Lam W, Xu D (2018a) Robust perimeter control for two urban regions with macroscopic fundamental diagrams: A control-lyapunov function approach. Transportation Res. Part B Methodological 117:687–707.CrossrefGoogle Scholar
  • Zhong R, Huang Y, Chen C, Lam W, Xu D, Sumalee A (2018b) Boundary conditions and behavior of the macroscopic fundamental diagram based network traffic dynamics: A control systems perspective. Transportation Res. Part B Methodological 111:327–355.CrossrefGoogle Scholar
  • Zhong R, Xiong J, Huang Y, Sumalee A, Chow AH, Pan T (2020) Dynamic system optimum analysis of multi-region macroscopic fundamental diagram systems with state-dependent time-varying delays. IEEE Trans. Intelligent Transportation Systems 21:4000–4016.CrossrefGoogle Scholar
  • Zhong R, Xiong J, Huang Y, Zheng N, Lam WH, Pan T, He B (2021) Dynamic user equilibrium for departure time choice in the basic trip-based model. Transportation Res. Part C Emerging Tech. 128:103190.CrossrefGoogle Scholar
  • Zhou D, Gayah VV (2021) Model-free perimeter metering control for two-region urban networks using deep reinforcement learning. Transportation Res. Part C Emerging Tech. 124:102949.CrossrefGoogle Scholar
  • Zhou Z, De Schutter B, Lin S, Xi Y (2016) Two-level hierarchical model-based predictive control for large-scale urban traffic networks. IEEE Trans. Control Systems Tech. 25(2):496–508.CrossrefGoogle Scholar
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