Performance Analysis of Centralized and Distributed Systems for Urban Traffic Control

In this paper, the performance of systems for urban traffic control is analyzed with consideration of real-time variations and uncertainties in traffic flow. The control systems range from centralized to semicentralized to distributed. The centralized system is represented by the global brute force and genetic optimization algorithms; the semicentralized system is represented by a linear quadratic regulator formulation; and the recently proposed max-pressure controller represents the distributed system. The control systems were applied to networks with different topology and demand levels, and the network performances were evaluated through a Monte Carlo stochastic simulation framework that incorporates various sources of variations that mimic the traffic variability observed in the real world. As expected, the brute force solutions outperformed the others, although the differences between the performances of different systems were not as significant as expected. It was found that the primary benefit gained from the brute force solution came from the setting of signal offsets. This result suggests that calculation of offsets plays an important role in bridging the gap between the computationally demanding centralized systems and the parsimonious distributed systems.