Transit-MP: Transit-Prioritized Max-Pressure Control in Sparse Connected Vehicle Environments

TL;DR

This work extends max-pressure traffic signal control to transit-prioritized operations in partially connected vehicle environments. By introducing occupancy-weighted upstream pressures and transit-dwell-aware downstream considerations, Transit-MP achieves network stability under partial CV observations and prioritizes high-occupancy transit flows. To address spillovers in sparse CV regimes, the authors propose mTransit-MP, which leverages historical data to estimate pressures when real-time CV data is missing, and they prove queue-starvation immunity. Extensive SUMO-based evaluation on a real Amsterdam corridor shows Transit-MP and especially mTransit-MP outperform existing CV-based MP controllers in delays, spillovers, and throughput, with mTransit-MP offering robust performance under CV data sparsity and estimation errors. These results suggest practical benefits for multi-modal traffic management in urban networks with limited CV penetration and evolving transit operations.

Abstract

Max-pressure (MP) control stands out among real-time network traffic signal control methods due to its simplicity, decentralized nature, and theoretical stability. However, existing MP control methods have limited consideration of public transportation and do not address the network stability problem of transit-prioritized MP in partially connected vehicle (CV) environments. In this study, we propose Transit-MP, which realizes transit-prioritized MP control in partially CV environments by considering real-time vehicle occupancy and the impact of transit dwell at stations. Theoretically, we demonstrate that Transit-MP, while using different traffic state measures for upstream and downstream links for pressure calculation, still achieves road network stability even in partially CV environments. Note that for MP controllers in sparse CV environments, some movements may have missing CV observations, leading to link spillovers, which create the queue starvation phenomenon: a movement no longer receives the green phase despite the queue spillover. Therefore, we further propose a modified Transit-MP (mTransit-MP) that incorporates historical traffic data to address this issue. We rigorously prove that the proposed mTransit-MP can effectively avoid the queue starvation phenomenon. Experimental results on a real-world corridor in Amsterdam with 15 transit lines and 31 stations show that our method significantly reduces the real-time vehicle and spillover count, and improves delays for both private vehicles and transit vehicles compared to a state-of-the-art MP controller for transit signal priority. In sparse CV environments, our mTransit-MP is effective in mitigating link spillovers while enhancing the overall performance of multi-modal traffic.

Figures (11)

• Figure 1: Network definition
• Figure 2: Vehicle information for $w_{i,o}(t)$
• Figure 3: Estimated total delay of vehicles on the link when no CVs are observed.
• Figure 4: Queue starvation phenomenon due to lack of CV observations.
• Figure 5: Real-world corridor with multi-modal traffic.

Theorems & Definitions (14)

• Theorem 1: Necessary condition of CV-MP stability in partially CV environments tan2025cvmp
• Lemma 1: Upper bounds of related parameters tan2025cvmp
• Definition 1: Admissible demand region
• Lemma 2: Properties of weights of Transit-MP
• proof
• Definition 2: Traffic network stability neely2022stochasticli2019positiontan2025cvmp
• Definition 3: Lyapunov function
• Lemma 3: Sufficient condition for traffic network stability
• Theorem 2: Stability of Transit-MP in partially CV environments
• proof