Optimizing distribution of metered traffic flow in perimeter control: Queue and delay balancing approaches

Abstract Perimeter traffic flow control based on the macroscopic or network fundamental diagram provides the opportunity of operating an urban traffic network at its capacity. Because perimeter control operates on the basis of restricting inflow via reduced green times at selected entry (gated) links, vehicles on those links may be subject to queuing and delay. The experienced delay or resulting queue lengths depend on the adopted policy for the distribution of the inflows and corresponding green times at the gated links. The chosen policy may have a significant impact on the traffic system under control. For example, managing queue lengths may reduce the interference with upstream traffic whereas the management of delays may improve users’ perception with respect to equity and fairness. In this paper, an approach has been proposed to distribute the gated flow based on the queue lengths or experienced delay at the gated signalized junctions. This is in contrast to standard practice that distributes inflows proportionally to the gated links’ saturation flows. Perimeter control is then evaluated in a microscopic simulator for a realistic traffic network and compared in three configurations against fixed-time: perimeter control without queue or delay management; perimeter control with relative queue balancing; and perimeter control with delay balancing. It has been found that managing the queues at the gated links not only improves the overall network performance but also reduces the possibility of queue propagation to the upstream junctions. This improves traffic flow outside the protected network by managing the queue propagation at the gated links and reducing the possibility of queue spill-back to upstream intersections. In addition, the results indicate that perimeter control with delay balancing has a similar performance as the case without queue or delay management being a suitable approach for flow distribution among the gated links. In the scenarios with perimeter control with either queue or delay balancing the gap between the ordered flow by the controller and the actual flow crossing the stop-line at the gated links reduced remarkably.

[1]  Serge P. Hoogendoorn,et al.  Macroscopic Fundamental Diagram for pedestrian networks: Theory and applications , 2018, Transportation Research Part C: Emerging Technologies.

[2]  Jorge A. Laval,et al.  Impact of buses on the macroscopic fundamental diagram of homogeneous arterial corridors , 2018 .

[3]  Markos Papageorgiou,et al.  Integrated feedback ramp metering and mainstream traffic flow control on motorways using variable speed limits , 2014 .

[4]  Meead Saberi,et al.  Connecting Networkwide Travel Time Reliability and the Network Fundamental Diagram of Traffic Flow , 2013 .

[5]  Roberto Cominetti,et al.  A Newton’s method for the continuous quadratic knapsack problem , 2014, Math. Program. Comput..

[6]  Nikolas Geroliminis,et al.  Macroscopic modelling and robust control of bi-modal multi-region urban road networks , 2017 .

[7]  Nikolas Geroliminis,et al.  Multiple Concentric Gating Traffic Control in Large-Scale Urban Networks , 2015, IEEE Transactions on Intelligent Transportation Systems.

[8]  Jian Guo,et al.  Evaluating semi-cooperative Nash/Stackelberg Q-learning for traffic routes plan in a single intersection , 2020 .

[9]  N. Geroliminis,et al.  A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks , 2014 .

[10]  Markos Papageorgiou,et al.  Real-time estimation of vehicle-count within signalized links , 2008 .

[11]  Nikolaos Geroliminis,et al.  Perimeter and boundary flow control in multi-reservoir heterogeneous networks , 2013 .

[12]  Victor L. Knoop,et al.  Traffic-responsive signals combined with perimeter control: investigating the benefits , 2019, Transportmetrica B: Transport Dynamics.

[13]  Francesco Corman,et al.  Macroscopic fundamental diagrams for train operations - are we there yet? , 2019, 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[14]  Andreas Hegyi,et al.  Linear MPC-Based Urban Traffic Control Using the Link Transmission Model , 2020, IEEE Transactions on Intelligent Transportation Systems.

[15]  Markos Papageorgiou,et al.  A multivariable regulator approach to traffic-responsive network-wide signal control , 2000 .

[16]  Wei Ni,et al.  City-wide traffic control: Modeling impacts of cordon queues , 2020, Transportation Research Part C: Emerging Technologies.

[17]  Rachid Bouyekhf,et al.  Modeling and entropy based control of urban transportation network , 2013 .

[18]  Nikolaos Geroliminis,et al.  Enhancing model-based feedback perimeter control with data-driven online adaptive optimization , 2017 .

[19]  Vikash V. Gayah,et al.  On the impacts of locally adaptive signal control on urban network stability and the Macroscopic Fundamental Diagram , 2014 .

[20]  Nikolas Geroliminis,et al.  Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks , 2020 .

[21]  Nan Zheng,et al.  Heterogeneity aware urban traffic control in a connected vehicle environment: A joint framework for congestion pricing and perimeter control , 2019, Transportation Research Part C: Emerging Technologies.

[22]  Jack Haddad Optimal coupled and decoupled perimeter control in one-region cities , 2017 .

[23]  Vikash V. Gayah,et al.  Clockwise Hysteresis Loops in the Macroscopic Fundamental Diagram , 2010 .

[24]  Jack Haddad Optimal perimeter control synthesis for two urban regions with aggregate boundary queue dynamics , 2017 .

[25]  Markos Papageorgiou,et al.  Feedback-Based Integrated Motorway Traffic Flow Control With Delay Balancing , 2017, IEEE Transactions on Intelligent Transportation Systems.

[26]  Nikolas Geroliminis,et al.  Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach , 2013, IEEE Transactions on Intelligent Transportation Systems.

[27]  Markos Papageorgiou,et al.  Freeway ramp metering: an overview , 2002, IEEE Trans. Intell. Transp. Syst..

[28]  N. Geroliminis,et al.  Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings - eScholarship , 2007 .

[29]  Markos Papageorgiou,et al.  Efficiency and equity properties of freeway network-wide ramp metering with AMOC , 2004 .

[30]  Jean-Loup Farges,et al.  THE PRODYN REAL TIME TRAFFIC ALGORITHM , 1983 .

[31]  Markos Papageorgiou,et al.  Controller Design for Gating Traffic Control in Presence of Time-delay in Urban Road Networks , 2015 .

[32]  Markos Papageorgiou,et al.  Queuing under perimeter control: Analysis and control strategy , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[33]  Nikolas Geroliminis,et al.  Cooperative traffic control of a mixed network with two urban regions and a freeway , 2013 .

[34]  Zhongsheng Hou,et al.  Perimeter Control of Urban Traffic Networks Based on Model-Free Adaptive Control , 2021, IEEE Transactions on Intelligent Transportation Systems.

[35]  David M Levinson,et al.  Balancing Efficiency and Equity of Ramp Meters , 2005 .

[36]  Nathan H. Gartner OPAC: Strategy for Demand-responsive Decentralized Traffic Signal Control , 1990 .

[37]  Hai Le Vu,et al.  Applications of the Generalized Macroscopic Fundamental Diagram , 2015 .

[38]  Markos Papageorgiou,et al.  Exploiting the fundamental diagram of urban networks for feedback-based gating , 2012 .

[39]  T. Friesz,et al.  A robust optimization approach for dynamic traffic signal control with emission considerations , 2012, 1211.4865.

[40]  Kun Xie,et al.  An advanced deep learning approach to real-time estimation of lane-based queue lengths at a signalized junction , 2019 .

[41]  Markos Papageorgiou,et al.  Balancing of Queues or Waiting Times on Metered Dual-Branch On-Ramps , 2011, IEEE Transactions on Intelligent Transportation Systems.

[42]  Carlos F. Daganzo,et al.  Urban Gridlock: Macroscopic Modeling and Mitigation Approaches , 2007 .

[43]  K. Wood Gating traffic into congested areas , 1993 .

[44]  Markos Papageorgiou,et al.  Optimal Motorway Traffic Flow Control Involving Variable Speed Limits and Ramp Metering , 2010, Transp. Sci..

[45]  Markos Papageorgiou,et al.  Mainstream Traffic Flow Control of merging motorways using Variable Speed Limits , 2011, 2011 19th Mediterranean Conference on Control & Automation (MED).

[46]  Peter Koonce,et al.  Statistical Study of the Impact of Adaptive Traffic Signal Control on Traffic and Transit Performance , 2013 .

[47]  Vikash V Gayah,et al.  On the existence of network Macroscopic Safety Diagrams: Theory, simulation and empirical evidence , 2018, PloS one.