Throughput-Delay Tradeoff Management for Partially Connected Networks via Lyapunov Drift Optimization

Journal article, 2026

Network-level traffic signal control is an effective way to increase throughput and reduce congestion. The max-pressure algorithm, known for maximizing network throughput, has been widely studied. However, it requires accurate queue length and turn ratio measurements, and its theoretical guarantee is limited to feasible demand (i.e., demand within the capacity region) under the assumption of infinite queue capacity. To overcome these limitations, this study proposes a distributed joint admission and signal control algorithm for finite-capacity networks with both connected and regular vehicles. By using feedback from connected vehicles, the algorithm estimates queue lengths and turn ratios, reducing reliance on precise measurements. It also adaptively adjusts input flow rates to prevent oversaturation and ensure demand feasibility, even under high-demand conditions, while optimizing signal phases to ensure analytic performance. Using a Lyapunov drift optimization approach, we analytically prove a [O(1V), O(V)] tradeoff between throughput and delay and establish degradation bounds that quantify the impact of queue length estimation errors on network performance. Simulations in a network with 256 origin-destination pairs show up to a 16.3% increase in throughput and reduced delays, especially in high-demand settings. The method also demonstrates strong resilience to sudden demand changes and incidents, ensuring quick recovery.