DEVELOPING A LARGE-SCALE URBAN DECISION SUPPORT SYSTEM

A large-scale Decision Support System (DSS) is being developed and will be applied for Beijing city in China. The main purpose is to be able to propose best suitable measures for a given (either recurrent or non-recurrent) traffic situation, and to apply it to a real-life traffic management, with focus on the application around the Olympics Area. A major issue for operational management is to be able fast to recognize primary problems and to be quick to recommend/retrieve corresponding solutions. This paper proposes a novel self-learning approach using conjointly expert knowledge-based choice and case-based reasoning. Key aspects to support such process include: (a) problem identification that is based on a mesoscopic large-scale network dynamic simulation; (b) measures that have been successfully implemented in a priori cases would serve as new initial scenarios to the new situations, and (c) measure evaluation that can be performed according to performance indictors. Effective scenarios (measure to problem) are stored into KBEST (knowledge-based expert system) and made available for offline and online calls. System building and a calibration process are being followed, and an implementation of such system to an incident management and route guidance is foreseen and being designed.