Effects of real-time and potential traffic congestion on network throughput
To reduce traffic congestion and improve network throughput, this study focuses on the dynamic routing process by constructing a unified path-cost function that integrates both a real-time traffic congestion index (derived from instantaneous node queue lengths) and a potential traffic congestion index (estimated from the expected number of future paths traversing each node based on active routing decisions). Through extensive simulations on Barabási–Albert scale-free network and Erdös–Rényi random network, we systematically investigate how these two types of congestion information affect network throughput. The results reveal that real-time traffic congestion is more significant than potential traffic congestion in improving network throughput. By comparison with five routing algorithms, it is found that the routing strategy considering both real-time and potential traffic congestion are more efficient. The main contribution of this work is to reveal the impacts of real-time and potential congestion on network throughput, clearly establishing their primary-secondary relationship in throughput control, and providing empirical guidelines and parameter-tuning recommendations for hybrid routing designs that combine immediate responsiveness with predictive awareness.