pith. sign in

arxiv: 2604.07840 · v1 · submitted 2026-04-09 · 📡 eess.SY · cs.SY

Distributive Perimetral Queue Balancing Mechanisms: Towards Equitable Urban Traffic Gating and Fair Perimeter Control

Kevin Riehl , Lea K\"unstler , Ying-Chuan Ni , Anastasia Psarou , Shaimaa K. El-Baklish , Anastasios Kouvelas , Michail A. Makridis This is my paper

Pith reviewed 2026-05-10 17:41 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords perimeter controlqueue balancingtraffic fairnessurban traffic managementequitable gatingheterogeneous demandintelligent transportation systems
0
0 comments X

The pith

Queue balancing mechanisms match conventional perimeter control efficiency while improving fairness across entry points in uneven urban demand.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper establishes that perimeter control, which meters vehicle entry into congested city centers based on aggregate network conditions, can incidentally reduce both total travel time and internal delays while also improving several fairness measures. It then proposes explicit queue balancing strategies that actively equalize queue lengths at different perimeter points. These strategies deliver the same system-wide performance gains as standard methods yet produce better fairness outcomes, especially when traffic demand varies sharply across entry locations. The evaluation rests on a large-scale microscopic simulation of San Francisco's Financial District. If correct, the work shows that traffic gating can be redesigned to address equity without trading off efficiency.

Core claim

Conventional perimeter control reduces total and internal delays while also improving Harsanyian, Rawlsian, Utilitarian, and Egalitarian fairness metrics. Queue balancing strategies achieve comparable delay reductions but yield additional measurable fairness gains, particularly under heterogeneous demand where congestion is unevenly distributed across entry points.

What carries the argument

Explicit queue balancing mechanisms that adjust perimeter inflows to equalize queue lengths at entry points while respecting aggregate network dynamics.

If this is right

  • Perimeter control can serve both efficiency and fairness goals simultaneously.
  • Queue balancing provides a practical way to address uneven delay distributions without new infrastructure.
  • The approach supports higher user acceptance for intelligent transportation systems by reducing perceived inequity.
  • The framework applies directly to cities with spatially uneven demand patterns.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar balancing logic could be tested on other gated urban systems such as bridge or tunnel access controls.
  • Real-time sensor data could be used to dynamically weight the fairness objectives in the control law.
  • Extending the method to multi-modal networks including transit and freight might reveal new trade-offs.

Load-bearing premise

The four selected fairness metrics capture what counts as equitable outcomes for individual drivers and the microscopic simulation of the San Francisco network accurately represents real heterogeneous demand and behavior.

What would settle it

A controlled simulation run or field test in which queue balancing produces higher total network delay than conventional perimeter control without a clear corresponding improvement in the four fairness metrics.

Figures

Figures reproduced from arXiv: 2604.07840 by Anastasia Psarou, Anastasios Kouvelas, Kevin Riehl, Lea K\"unstler, Michail A. Makridis, Shaimaa K. El-Baklish, Ying-Chuan Ni.

Figure 1
Figure 1. Figure 1: Perimeter Control & Signal Plans. at its capacity Ci at the optimal accumulation, meaning Fi(n ∗ i ) = Ci ; due to congestion that arises in a too crowded zone, the flow decreases for higher accumulations. The goal of perimeter control is to limit the number of vehicles in a zone to achieve congestion-free, flow-maximising traffic conditions in the road network of that zone. This is achieved by gating the … view at source ↗
Figure 2
Figure 2. Figure 2: Case Study: Three zones in San Francisco’s Financial District. III. METHODS A. Distributive, Perimetral Queue Balancing Mechanism The feedback-based gating algorithm determines an in￾coming rate r E i for each zone Zi . Doing so, it provides an available budget, the total admissible incoming green ratio R avail i , for all intersections of that zone: R avail i = ∑ j∈Ji r E i (6) The idea of the queue balan… view at source ↗
Figure 3
Figure 3. Figure 3: Vehicle Accumulation [%] at 09:00 (Uncontrolled). vs. 25.82 km/h uncontrolled) [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Perimeter Control Implications on Fundamentals, Queue Lengths and Loss-Times. Figures shown for different zones (coloured equals with perimeter control, gray uncontrolled) for one scenario qualitatively (similar results for different random seeds); all forms of perimeter control (Gating, MaxMin- and Proportional Queue Balancing yield similar results). Black solid lines in first two rows represent the zone’… view at source ↗
read the original abstract

Perimeter control is an effective urban traffic management strategy that regulates inflow to congested urban regions using aggregate network dynamics. While existing approaches primarily optimize system-level efficiency, such as total travel time or network throughput, they often overlook equity considerations, leading to uneven delay distributions across entry points. This work integrates fairness objectives into perimeter control design through explicit queue balancing mechanisms.A large-scale, microscopic case study of the Financial District in the San Francisco urban network is used to evaluate both performance and implementation challenges. The results demonstrate conventional perimeter control not only reduces total and internal delays but can also improve fairness metrics (Harsanyian, Rawlsian, Utilitarian, Egalitarian). Building on this observation, queue balancing strategies match conventional performance while yielding measurable fairness improvements, especially in heterogeneous demand scenarios, where congestion is unevenly distributed across entry points. The proposed framework contributes toward equitable control design for emerging intelligent transportation systems and higher user acceptance for those.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes integrating fairness objectives into perimeter control via distributive queue balancing mechanisms. A large-scale microscopic simulation case study on the San Francisco Financial District network shows that conventional perimeter control reduces total and internal delays while also improving Harsanyian, Rawlsian, Utilitarian, and Egalitarian fairness metrics; the proposed queue balancing strategies match this performance on delay metrics but deliver additional fairness gains, particularly under heterogeneous demand across entry points.

Significance. If the simulation results hold under real-world conditions, the work advances equitable urban traffic management by showing that fairness can be improved without sacrificing efficiency, which may increase public acceptance of gating strategies in intelligent transportation systems. The use of multiple established fairness metrics and emphasis on heterogeneous demand scenarios adds practical value.

major comments (2)
  1. [Case study evaluation] The case study evaluation (abstract and associated results section): The headline fairness improvements under heterogeneous demand rest entirely on the fidelity of the San Francisco microscopic model, yet no calibration against field counts, loop-detector data, route-choice surveys, or compliance rates is described, nor are sensitivity sweeps on demand variance reported. This is load-bearing for the central claim that queue balancing yields measurable fairness gains.
  2. [Results presentation] Results presentation (abstract and results section): No error bars, confidence intervals, or statistical significance tests accompany the reported fairness metric deltas, and baseline comparisons appear limited to conventional perimeter control without additional controls for demand heterogeneity. This weakens verification that the fairness improvements are robust rather than model artifacts.
minor comments (2)
  1. [Abstract] The abstract states positive results but does not quantify the magnitude of fairness improvements or delay reductions, which would help readers assess practical significance.
  2. [Fairness metrics] Notation for the four fairness metrics could be introduced with explicit formulas in the main text rather than relying on references alone, to improve readability for the control-systems audience.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below and describe the revisions planned for the next version.

read point-by-point responses

  1. Referee: [Case study evaluation] The case study evaluation (abstract and associated results section): The headline fairness improvements under heterogeneous demand rest entirely on the fidelity of the San Francisco microscopic model, yet no calibration against field counts, loop-detector data, route-choice surveys, or compliance rates is described, nor are sensitivity sweeps on demand variance reported. This is load-bearing for the central claim that queue balancing yields measurable fairness gains.

    Authors: We acknowledge that the manuscript does not provide a dedicated calibration section against field data. The network model follows standard publicly documented topologies and demand patterns used in prior studies of the same area. To strengthen the central claim, the revised manuscript will add a sensitivity analysis subsection that varies demand heterogeneity levels and reports the resulting fairness metric ranges. We will also include an explicit limitations paragraph noting the absence of field calibration. revision: partial

  2. Referee: [Results presentation] Results presentation (abstract and results section): No error bars, confidence intervals, or statistical significance tests accompany the reported fairness metric deltas, and baseline comparisons appear limited to conventional perimeter control without additional controls for demand heterogeneity. This weakens verification that the fairness improvements are robust rather than model artifacts.

    Authors: The presented results derive from deterministic single-run simulations chosen to isolate strategy effects. We agree that statistical characterization would improve verifiability. The revision will incorporate multiple replications using varied random seeds, add error bars and confidence intervals to the fairness metric plots, and expand the baseline set to include explicit controls for different degrees of demand heterogeneity. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results derived from independent simulation experiments

full rationale

The paper evaluates perimeter control and queue-balancing strategies exclusively through a large-scale microscopic simulation of the San Francisco Financial District network. Fairness metrics (Harsanyian, Rawlsian, Utilitarian, Egalitarian) and delay reductions are computed directly from simulation outputs under homogeneous and heterogeneous demand scenarios. No equations, parameter fittings, or derivations are shown that reduce a claimed prediction to its own inputs by construction, nor does any load-bearing step rely on self-citation chains or imported uniqueness theorems. The central claims therefore remain externally falsifiable against the simulation benchmarks rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; relies on standard traffic flow simulation assumptions and imported economic fairness concepts without detailing their grounding.

pith-pipeline@v0.9.0 · 5500 in / 1165 out tokens · 119925 ms · 2026-05-10T17:41:11.405949+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages

  1. [1]

    Urban gridlock: Macroscopic modeling and mitigation approaches,

    C. F. Daganzo, “Urban gridlock: Macroscopic modeling and mitigation approaches,”Transportation Research Part B: Methodological, vol. 41, no. 1, pp. 49–62, 2007

    work page 2007

  2. [2]

    A hybrid strategy for real-time traffic signal control of urban road networks,

    A. Kouvelas, M. Papageorgiou, K. Aboudolas, and E. Kosmatopoulos, “A hybrid strategy for real-time traffic signal control of urban road networks,”IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 884–894, 2011

    work page 2011

  3. [3]

    On the stability of traffic perimeter control in two-region urban cities,

    J. Haddad and N. Geroliminis, “On the stability of traffic perimeter control in two-region urban cities,”Transportation Research Part B: Methodological, vol. 46, no. 9, pp. 1159–1176, 2012

    work page 2012

  4. [4]

    Optimal perimeter control for two urban regions with macroscopic fundamental diagrams: A model predictive approach,

    N. Geroliminis, J. Haddad, and M. Ramezani, “Optimal perimeter control for two urban regions with macroscopic fundamental diagrams: A model predictive approach,”IEEE Transactions on Intelligent Trans- portation Systems, vol. 14, no. 1, pp. 348–359, 2012

    work page 2012

  5. [5]

    Exploiting the fundamental diagram of urban networks for feedback-based gating,

    M. Keyvan-Ekbatani, A. Kouvelas, I. Papamichail, and M. Papageor- giou, “Exploiting the fundamental diagram of urban networks for feedback-based gating,”Transportation Research Part B: Methodolog- ical, vol. 46, no. 10, pp. 1393–1403, 2012

    work page 2012

  6. [6]

    Perimeter and boundary flow control in multi-reservoir heterogeneous networks,

    K. Aboudolas and N. Geroliminis, “Perimeter and boundary flow control in multi-reservoir heterogeneous networks,”Transportation Research Part B: Methodological, vol. 55, pp. 265–281, 2013

    work page 2013

  7. [7]

    Robust perimeter control design for an urban region,

    J. Haddad and A. Shraiber, “Robust perimeter control design for an urban region,”Transportation Research Part B: Methodological, vol. 68, pp. 315–332, 2014

    work page 2014

  8. [8]

    Adaptive perimeter control for multi- region accumulation-based models with state delays,

    J. Haddad and Z. Zheng, “Adaptive perimeter control for multi- region accumulation-based models with state delays,”Transportation Research Part B: Methodological, vol. 137, pp. 133–153, 2020

    work page 2020

  9. [9]

    Enhancing model-based feedback perimeter control with data-driven online adap- tive optimization,

    A. Kouvelas, M. Saeedmanesh, and N. Geroliminis, “Enhancing model-based feedback perimeter control with data-driven online adap- tive optimization,”Transportation Research Part B: Methodological, vol. 96, pp. 26–45, 2017

    work page 2017

  10. [10]

    Two-layer adap- tive signal control framework for large-scale dynamically-congested networks: Combining efficient max pressure with perimeter control,

    D. Tsitsokas, A. Kouvelas, and N. Geroliminis, “Two-layer adap- tive signal control framework for large-scale dynamically-congested networks: Combining efficient max pressure with perimeter control,” Transportation Research Part C: Emerging Technologies, vol. 152, p. 104128, 2023

    work page 2023

  11. [11]

    N-mp: A network-state-based max pressure algorithm incorporating regional perimeter control,

    H. Liu and V . V . Gayah, “N-mp: A network-state-based max pressure algorithm incorporating regional perimeter control,”Transportation Research Part C: Emerging Technologies, vol. 168, p. 104725, 2024

    work page 2024

  12. [12]

    Perimeter control with real-time location-varying cordon,

    Y . Li, R. Mohajerpoor, and M. Ramezani, “Perimeter control with real-time location-varying cordon,”Transportation Research Part B: Methodological, vol. 150, pp. 101–120, 2021

    work page 2021

  13. [13]

    Perimeter control and route guidance of multi-region mfd systems with boundary queues using colored petri nets,

    H. Fu, S. Chen, K. Chen, A. Kouvelas, and N. Geroliminis, “Perimeter control and route guidance of multi-region mfd systems with boundary queues using colored petri nets,”IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 12 977–12 999, 2021

    work page 2021

  14. [14]

    Percolation- based dynamic perimeter control for mitigating congestion propagation in urban road networks,

    H. Hamedmoghadam, N. Zheng, D. Li, and H. L. Vu, “Percolation- based dynamic perimeter control for mitigating congestion propagation in urban road networks,”Transportation Research Part C: Emerging Technologies, vol. 145, p. 103922, 2022

    work page 2022

  15. [15]

    Improving deep reinforcement learning- based perimeter metering control methods with domain control knowl- edge,

    D. Zhou and V . V . Gayah, “Improving deep reinforcement learning- based perimeter metering control methods with domain control knowl- edge,”Transportation Research Record, vol. 2677, no. 7, pp. 384–405, 2023

    work page 2023

  16. [16]

    Evaluating the effectiveness and transferability of a data- driven two-region perimeter control method using microsimulation,

    ——, “Evaluating the effectiveness and transferability of a data- driven two-region perimeter control method using microsimulation,” Transportation Research Record, vol. 2678, no. 9, pp. 435–451, 2024

    work page 2024

  17. [17]

    Decentralized multi-region perimeter control in complex urban environments using reinforcement and imitation learning,

    E. Kampitakis and E. I. Vlahogianni, “Decentralized multi-region perimeter control in complex urban environments using reinforcement and imitation learning,”Transportation Research Part C: Emerging Technologies, vol. 178, p. 105253, 2025

    work page 2025

  18. [18]

    Understanding congestion propagation by combining percolation theory with the macroscopic fundamental diagram,

    L. Amb ¨uhl, M. Menendez, and M. C. Gonz ´alez, “Understanding congestion propagation by combining percolation theory with the macroscopic fundamental diagram,”Communications Physics, vol. 6, no. 1, p. 26, 2023

    work page 2023

  19. [19]

    Study on the number and location of measurement points for an mfd perimeter control scheme: a case study of zurich,

    J. Ortigosa, M. Menendez, and H. Tapia, “Study on the number and location of measurement points for an mfd perimeter control scheme: a case study of zurich,”EURO Journal on Transportation and Logistics, vol. 3, no. 3, pp. 245–266, 2015

    work page 2015

  20. [20]

    Towards fair roads–why we should & how to improve the fairness in traffic engineering,

    K. Riehl, A. Kouvelas, and M. Makridis, “Towards fair roads–why we should & how to improve the fairness in traffic engineering,”arXiv preprint arXiv:2408.01309, 2024

    work page arXiv 2024

  21. [21]

    Quantitative fairness – a framework for the design of equitable cybernetic societies,

    K. Riehl, A. Kouvelas, and M. A. Makridis, “Quantitative fairness – a framework for the design of equitable cybernetic societies,”Computers in Human Behavior: Artificial Humans, p. 100236, 2025

    work page 2025

  22. [22]

    Alpha-fair large-scale urban network control: A perimeter control based on a macroscopic fundamental diagram,

    N. Moshahedi and L. Kattan, “Alpha-fair large-scale urban network control: A perimeter control based on a macroscopic fundamental diagram,”Transportation Research Part C: Emerging Technologies, vol. 146, p. 103961, 2023

    work page 2023

  23. [23]

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

    M. Keyvan-Ekbatani, R. C. Carlson, V . L. Knoop, and M. Papageor- giou, “Optimizing distribution of metered traffic flow in perimeter control: Queue and delay balancing approaches,”Control Engineering Practice, vol. 110, p. 104762, 2021

    work page 2021

  24. [24]

    Goppel, C

    A. Goppel, C. Mieth, and C. Neuh ¨auser,Handbuch Gerechtigkeit. Springer, 2016

    work page 2016

  25. [25]

    Mi- croscopic traffic simulation using sumo,

    P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y .-P. Fl¨otter¨od, R. Hilbrich, L. L ¨ucken, J. Rummel, P. Wagner, and E. Wiessner, “Mi- croscopic traffic simulation using sumo,” in2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 2575–2582

    work page 2018