Toward an Autonomic Architecture for Real-Time Traffic Network Management

Abstract

This article presents an autonomic-based architecture for real-time traffic management in congested urban transportation networks. The architecture assumes the availability of spatially distributed controllers in the network. Each controller is capable of monitoring the traffic within a predefined subnetwork and provides efficient control strategies for its traffic. Controllers are assumed to be able to share information on the observed traffic pattern and their control actions. In addition, controllers could be dynamically configured to operate in teams to develop integrated traffic management schemes that best cope with the observed traffic pattern in the network. The article presents the results of a set of off-line experiments that examine the performance of the proposed architecture. The experiments evaluate the most efficient team-formation strategies among controllers to mitigate a nonrecurrent traffic congestion situation in a typical highway network. The results show that more efficient traffic management strategies could be obtained through collaboration among individual controllers and could result in considerable travel time savings.

Keywords:

• Autonomic
• Distributed Systems
• Dynamic Traffic Assignment
• Traffic Network Management

Acknowledgments

The research presented in this article is supported in part by the Bobby B. Lyle School of Engineering at Southern Methodist University and the National Science Foundation via grants numbers IIP-0758579, CNS-0855087, and IIP-1032048, and it is conducted as part of the NSF Center for Autonomic Computing.