Applications of Petri networks and fuzzy logic to advanced traffic management systems

The development of advanced simulation tools and control algorithms to assess the benefits of Advance Traffic Management Systems (ATMS) is considered. It includes the development of on-line, adaptive strategies for traffic signal control, and the evolution of a traffic network model to test these real-time signal control strategies.; The development of a mathematical modular framework based on Petri Network theory to model a traffic intersection is presented. Traffic intersections are characterized as event driven asynchronous systems. Petri networks have been utilized to model discrete event systems; further analysis of their structure can reveal information relevant to the concurrency, parallelism, synchronization, and deadlock avoidance issues. The Petri-net based model of a generic traffic junction is presented, its deadlock avoidance property proven, and a proposed software pseudocode for simulations on a multiprocessor system is demonstrated.; The development and implementation of a real-time, traffic adaptive control scheme based on fuzzy logic through Wide Area Detection system (WADs) is presented. Through WADs based image processing, fuzzy logic can be used for a real-time traffic adaptive signal control scheme. Fuzzy control logic allows linguistic and inexact traffic data to be manipulated as a useful tool in designing signal timing plans. The fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategy based on a priori verbal communication. The implementation of fuzzy logic controller for a traffic network is introduced. Comparisons are made between implementations of the fuzzy logic controller and the actuated controller in an isolated intersection. The results obtained from the application of the fuzzy logic controller are also compared with those corresponding to a pretimed controller for the coordinated intersections. Simulation results from the comparisons indicate the performance of the system is better under the fuzzy logic controller.; Integration of the aforementioned schemes into an ATMS framework will lead to real-time adjustment of the traffic control signals, resulting in significant reduction in traffic congestion.