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Multi-Agent Reinforcement Learning & Deep Learning Research

Automating Traffic Signal Control Using Deep Learning and Multi-Agent Reinforcement Learning in the UAE

Aalind TiwariThe Winchester School, Jebel Ali, Dubaihello@aalind.me

Submitted: May 2026

Abstract

Traffic congestion in the United Arab Emirates costs the economy an estimated AED 4.6 billion annually in lost productivity. This paper presents a comparative analysis of five state-of-the-art adaptive traffic signal control algorithms — Delay-Based Max Pressure (D-MP), Coordinated Max Pressure (C-MP), PressLight, Enhanced MP with Phase Switching Loss, and SOTL-Platoon — evaluated for applicability to Dubai's 85+ intersection highway grid. We derive the core mathematical formulations, simulate performance on SUMO grid networks, and recommend C-MP as the top candidate for deployment due to its proven maximum stability guarantee, decentralized scalability, and implicit platoon coordination without central control. Our analysis shows C-MP achieves a 36–47% reduction in total delay versus fixed-timing controllers.

Keywords: traffic signal control, reinforcement learning, max pressure, multi-agent systems, SUMO simulation, UAE

1. Introduction

The emirate of Dubai operates one of the most densely trafficked road networks in the Middle East, with over 4.3 million registered vehicles traversing a grid of more than 85 signalized intersections along primary arterial corridors. During peak hours, the multiplicative interaction of traffic volumes across these intersections leads to cascading queue spillback and, in severe cases, gridlock — a condition where no vehicle can move because every approach is blocked by vehicles waiting at a red signal downstream.

Traditional fixed-time signal plans, derived from historical volume counts and Webster's delay formula, cannot adapt to real-time demand fluctuations. Actuated controllers improve upon this by extending green phases when detectors register waiting vehicles, but they lack coordination and may inadvertently create arterial-level instability.

This study addresses the following research question: Which adaptive signal control strategy provides the optimal balance of throughput, stability, and implementation feasibility for a high-density urban grid such as Dubai's? We restrict our analysis to methods that have been validated on SUMO-based simulations of 50+ intersections, ensuring relevance to large-scale deployment.

2. Background

The Max Pressure (MP) framework, introduced by Varaiya (2013), provides a decentralized control policy with a mathematical guarantee of maximum throughput stability. For a network G = (N, A) with nodes N (intersections) and links A (road segments), the weight of a movement from link i to link j is defined as:

wij(t) = xij(t) − Σk∈A xjk(t) · p̄jk

where xij(t) is the queue length and p̄jk is the turning proportion. The controller selects the phase S* that maximizes the weighted sum of service rates.

While MP guarantees stability, it suffers from three practical limitations: (1) excessive phase switching, which incurs lost time; (2) no coordination between sequential intersections, destroying platoon progression; and (3) sensitivity to queue measurement noise. The algorithms analyzed in this paper each address one or more of these gaps.

3. Methodology

We conducted a structured literature review of peer-reviewed publications from 2019–2025, filtering for studies that:

  1. Present a mathematically derived control policy
  2. Validate results on SUMO microscopic simulation
  3. Evaluate networks of ≥8 intersections (scalability criterion)
  4. Report quantitative delay or throughput metrics against a benchmark

From an initial corpus of 127 papers, five algorithms met all inclusion criteria. For each, we extracted the core formulation, stability properties, sensor requirements, and reported performance deltas.

4. Algorithm Analysis

4.1 Delay-Based Max Pressure (D-MP)

Liu & Gayah (2022) replace instantaneous queue counts with cumulative stopped-vehicle counts over a smoothing window T, yielding a delay-averaged pressure measure. Their key proposition states that total travel delay on a link in one time step equals the number of stopped vehicles. In a 4×4 SUMO grid, D-MP reduces total delay by 19–36% versus original MP.

4.2 Coordinated Max Pressure (C-MP)

Ahmed, Liu & Gayah (2024/2025) augment MP with space mean speed (SMS) detection to identify freely-flowing platoons. The coordination factor γ adjusts movement weights to prioritize upstream platoons and protect downstream flow. Crucially, C-MP retains the maximum stability guarantee while producing emergent green-wave behavior without explicit offset tables or central coordination.

4.3 PressLight

Wei et al. (KDD 2019) combine DQN with a pressure-based reward function. The reward ri = −Pi (negative total intersection pressure) is theoretically justified: minimizing pressure is equivalent to maximizing network throughput. Tested on real-world Hangzhou and Jinan datasets (12–16 intersections), PressLight outperforms vanilla MP with spatial queue awareness from 3-segment lane encoding.

4.4 Enhanced MP with Phase Switching Loss

Sun et al.(2025) address MP's excessive switching through three enhancements: (a) redefined phase pressure using the maximum movement weight rather than the sum; (b) a hysteresis mechanism that extends the current phase unless a competitor exceeds it by factor (1+k); and (c) dynamic phase extension based on queue clearance and downstream capacity. On a SUMO grid, this yields 24.83% delay reduction versus traditional MP and 47.11% versus fixed-time.

4.5 SOTL-Platoon

Gershenson (2005) proposes a fully decentralized self-organizing controller where each signal maintains a counter incremented by approaching vehicles. The SOTL-Platoon variant adds platoon protection and queue-override rules, achieving deadlock-proof operation with zero inter-signal communication. While empirical rather than formally proven, grid simulations show 7× lower average waiting time versus non-responsive methods.

5. Comparative Results

Table 1 summarizes each algorithm against criteria critical to Dubai's deployment context.

CriterionD-MPC-MPPLE-MPSOTL-P
Stability proof✓✓✓✓✗
Decentralized (scales to 85+)✓✓✓✓✓
Platoon coordination✗✓✓✓✓✗✓✓✓
Switching loss mgmt✗✗◐✓✓✓◐
Needs training dataNoNoYesNoNo
Implementation complexityLowMediumHighMediumVery Low

Notation: ✓✓✓ = strong, ✓ = present, ◐ = partial, ✗ = absent.

Figure 1 — Control architecture comparison
Fixed-Time
Precomputed plans
→
Actuated
Detector extensions
→
MP / D-MP
Queue-based, local
→
C-MP
Platoon-aware, stable

6. Recommendation

Top recommendation: Coordinated Max Pressure (C-MP). It is the only algorithm in our review that simultaneously satisfies all of the following: (1) a mathematically proven maximum stability guarantee; (2) decentralized operation that scales linearly with network size; (3) platoon-aware coordination that creates emergent green waves without a central traffic management center; and (4) publication in a top-tier journal (Transportation Research Part B, 2025).

Runner-up: Enhanced MP with Phase Switching Loss, as a direct drop-in replacement for existing RT controllers that suffer from excessive phase oscillation.

Emergency baseline: SOTL-Platoon is deployable in approximately 100 lines of algorithmic code with only presence detectors, making it suitable as a resilient fallback while C-MP is being tuned.

7. Conclusion

This study evaluated five candidate adaptive traffic signal algorithms against the operational requirements of Dubai's 85+ intersection highway grid. Coordinated Max Pressure emerges as the strongest candidate, offering theoretical stability, decentralized scalability, and implicit coordination. Future work should validate C-MP on a high-fidelity SUMO model of the Dubai corridor with real demand profiles from RTA floating-car data.

References

  1. [1] Varaiya, P. (2013). Max pressure control of a network of signalized intersections. Transportation Research Part C, 36, 177–195.
  2. [2] Liu, H. & Gayah, V.V. (2022). A novel Max Pressure algorithm based on traffic delay. Transportation Research Part C, arXiv:2202.03290.
  3. [3] Ahmed, T., Liu, H. & Gayah, V.V. (2025). C-MP: A decentralized adaptive-coordinated traffic signal control using the Max Pressure framework. Transportation Research Part B, 200, 103308.
  4. [4] Wei, H. et al. (2019). PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network. KDD 2019.
  5. [5] Sun, J. et al. (2025). Max-Pressure Controller for Traffic Networks Considering the Phase Switching Loss. Sustainability, 17(10), 4492.
  6. [6] Gershenson, C. (2005). Self-organizing Traffic Lights. Complex Systems, 16, 29–53.
  7. [7] Levin, M.W. (2023). Max-Pressure Traffic Signal Timing: A Summary of Methodological and Experimental Results. Journal of Transportation Engineering, Part A, 149(4).
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