Advanced Architecture for Real-Time Traffic Management

This research effort studies real-time traffic management for large-scale urban transportation networks. This research is the first attempt to adopt the autonomic control paradigm for real-time traffic management. The autonomic control paradigm is inspired by the human autonomic nervous system that handles complexity and uncertainties. It aims at realizing dynamic systems and applications capable of managing themselves with minimum human intervention. The proposed architecture adopts a hierarchal dynamic framework in which a set of distributed controllers are assumed to share information on traffic pattern as well as their control actions. These controllers are dynamically configured to operate either individually or in teams to develop integrated control strategies that best cope with the observed traffic pattern in the network. In order to examine the performance of the new architecture, a set of offline simulation-based experiments were conducted using hypothetical and real roadway networks. A traffic-simulation assignment model is used to evaluate the network performance considering recurrent and non-recurrent congestion scenarios. The results illustrate that more efficient traffic management plans can be obtained through allowing collaboration among the individual controllers. Travel time saving that ranges from 6%to 12%are observed especially during non-recurrent traffic congestion situations.

کلیدواژه ها:

Autonomic ، Real-Time Traffic Management ، Distributed Systems ، Dynamic Traffic Assignment