A fuzzy-based methodology for anticipating trend of incident traffic congestion on expressways

Abstract

Traffic control decisions for incident congestion management on expressways are often made in the face of uncertainty because it entails using many forms of both current and predicted traffic data and incident information to arrive at control decisions under critical-time pressure. For these reasons, an effective traffic control strategy during incidents often relies on techniques that deal efficiently with problems of uncertainty and imprecision. Motivated by this, the author has carried out a research project that develops a multi-stage Fuzzy Logic Controller (MS-FLC) as a tool to support traffic operator’s decision-making at the operational level. The research project aims at establishing a systematic procedure in deriving control actions for ramp control during incidents on expressways following fuzzy-logic approach. For proactive ramp control, the trend of traffic condition on expressways during incidents should be properly anticipated. This paper presents the first two stages of the MS-FLC: (1) evaluation of traffic condition upon incident occurrences, and (2) anticipation of traffic condition during incidents. The results show that the MS-FLC provides a systematic procedure in deriving control actions using fuzzy-based methodology, which possesses excellent capabilities in data-handling and knowledge representation to deliver linguistic expressions that is easy to understand by the operators for making decisions. With both current and anticipated types of information obtained from these two stages, the MS-FLC operates on both reactive and proactive control manners so as to enhance performance of the incident management on expressways.