Knowledge-based models for adaptive traffic management systems

Abstract This paper describes a general approach for real time traffic management support using knowledge based models. Recognizing that human intervention is usually required to apply the current automatic traffic control systems, it is argued that there is a need for an additional intelligent layer to help operators to understand traffic problems and to make the best choice of strategic control actions that modify the assumption framework of the existing systems. The need for an open architecture is stated, in order to allow users to modify decision criteria according to their experience, given that no skills are available yet to deal with real time strategy decision making. An architecture of knowledge is described that is oriented towards traffic management strategic advice applied in the TRYS system developed by the authors. This system has been installed for urban motorway control in several Spanish cities. Finally, an example of knowledge-based modeling, using TRYS, is presented in a case study where both the TRYS model and its operation are described. It is concluded that such an approach is feasible, and is compatible with existing state of the art traffic control systems.

[1]  Stephen G. Ritchie,et al.  A Real-Time Expert System for Freeway Incident Management in Orange County, California , 1993 .

[2]  M Boero.  KITS, A GENERAL APPROACH FOR KNOWLEDGE-BASED TRAFFIC CONTROL MODELS , 1993 .

[3]  John Wylie Lloyd,et al.  Foundations of Logic Programming , 1987, Symbolic Computation.

[4]  B. Chandrasekaran,et al.  Towards a Taxonomy of Problem Solving Types , 1983, AI Mag..

[5]  José Cuena,et al.  CASE PRESENTATION OF THE USE OF KNOWLEDGE BASED MODELS FOR TRAFFIC MANAGEMENT - MADRID , 1994 .

[6]  H Kirschfink,et al.  THE ROLE OF KNOWLEDGE-BASED MODELS IN TRAFFIC MANAGEMENT AND THEIR DESIGN , 1994 .

[7]  John McDermott,et al.  Preliminary steps toward a taxonomy of problem-solving methods , 1993 .

[8]  K. Brunnstein,et al.  Applications and impacts , 1994 .

[9]  Jean-Loup Farges,et al.  THE PRODYN REAL TIME TRAFFIC ALGORITHM , 1983 .

[10]  G Ambrosino,et al.  A GENERAL KNOWLEDGE-BASED ARCHITECTURE FOR TRAFFIC CONTROL : THE KITS APPROACH , 1992 .

[11]  Patrick Henry Winston,et al.  The psychology of computer vision , 1976, Pattern Recognit..

[12]  Nathan H. Gartner,et al.  OPAC: A DEMAND-RESPONSIVE STRATEGY FOR TRAFFIC SIGNAL CONTROL , 1983 .

[13]  Ira P. Goldstein,et al.  The FRL Manual , 1977 .

[14]  R. D. Bretherton,et al.  Recent enhancements to SCOOT-SCOOT Version 2.4 , 1990 .

[15]  Sandra Marcus,et al.  Automating Knowledge Acquisition for Expert Systems , 1988 .

[16]  P R Lowrie,et al.  The Sydney coordinated adaptive traffic system - principles, methodology, algorithms , 1982 .

[17]  R D Bretherton,et al.  SCOOT-a Traffic Responsive Method of Coordinating Signals , 1981 .

[18]  Ronald J. Brachman,et al.  An overview of the KL-ONE Knowledge Representation System , 1985 .

[19]  Elaine Kant,et al.  Programming expert systems in OPS5 , 1985 .

[20]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[21]  José Cuena.  Contributions to a Knowledge Oriented View of Software Design , 1992, AIFIPP.

[22]  B. Chandrasekaran.  Towards a Functional Architecture for Intelligence Based on Generic Information Processing Tasks , 1987, IJCAI.

[23]  Joshua Lederberg,et al.  Applications of Artificial Intelligence for Organic Chemistry: The DENDRAL Project , 1980 .

[24]  Dean L Deeter,et al.  A PROTOTYPE REAL-TIME EXPERT SYSTEM FOR SURFACE STREET TRAFFIC MANAGEMENT AND CONTROL , 1993 .