Abstract

This research describes an innovative distributed approach for the provision of real-time decision support to Transportation Management Center (TMC) operators for coordinated, multi-jurisdictional traffic congestion management on freeway and arterial networks. Coordinated responses among the agencies that share responsibilities for urban traffic management avoids the implementation of operations that may be conflicting or counter-productive.

A distributed software architecture, called CARTESIUS (Coordinated Adaptive Real-Time Expert System for Incident management in Urban Systems) was designed, developed and evaluated. CARTESIUS is composed of two interacting, real-time decision-support systems for TMC operator that are able to perform cooperative reasoning and resolve conflicts, for the analysis of non-recurring congestion and the formulation of suitable integrated control responses. The two agents support incident management operations for, respectively, a freeway and an adjacent arterial subnetwork. Each module interacts with a human operator in one of the agencies, is able to receive real-time traffic and control data, and provides the operator with control recommendations in response to the occurrence of incidents. The multi-decision making approach adopted by CARTESIUS reflects the spatial and administrative organization of traffic management agencies, providing a coordinated solution that attempts to satisfy all parties, preserves their own levels of authority, and reflects the inherent distribution of the decision-making power.

The structure of the distributed processing and the interaction between the agents is based on the Functionally Accurate, Cooperate (FA/C) paradigm, a distributed problem solving approach aimed at producing consistent global solutions even when complete and up-to-date information is not directly available to the agents, in order to reduce communication requirements and synchronization time delays.

The contribution of this research lies in demonstrating the validity of the assumption that satisficing control solutions can be efficiently obtained by relaxing the requirements that agents have shared access to all globally available information, and the application of theoretical principles of the FA/C paradigm to traffic control, through the development of CARTESIUS. The simulation-based validation of the system performance has demonstrated the effectiveness of such an approach in producing real-time, integrated traffic control solutions that reduce the adverse impact of incidents on traffic circulation, network-wide.

Abstract

This dissertation presents the vehicle reidentification problem formulated as a lexicographic optimization problem. The lexicographic optimization formulation is a preemptive multi-objective formulation that combines goal programming, classification, and Bayesian analysis techniques. The details of field implementation and data collection design are also presented. The solution of the vehicle reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of specific dynamic origin/destination demands as well as the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems) implementations of the approach using conventional surveillance infrastructure. Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions. A discussion is also presented of the application of section densities in a dynamic origin/destination demand estimation framework as an example of the usefulness of this approach. The use of existing surveillance infrastructure coupled with this approach could allow development of widespread applications in Intelligent Transportation Systems (ITS).