Fuzzy Inference System Framework to Prioritize the Deployment of Resources in Low Visibility Traffic Conditions

Abstract

Tasks related to traffic and safety operations under low visibility conditions remain a challenge for transportation agencies nationwide. Transportation managers are required to model weather and road variables to make operational decisions, while simultaneously prioritizing the deployment of available resources. This research presents a fuzzy inference system (FIS) framework to assist transportation managers with prioritizing the deployment of resources to areas of low visibility originated by fog. The framework is composed of three fuzzy systems that characterize fog occurrence, road risk conditions, and deployment of resources. Fuzzy logic is considered a decision-making technique that could be effectively applied when dealing with ambiguous, imprecise and complex variables like fog in multiple domains, including but not limited to weather, transportation, and resource allocation. The main benefit of this technique is the ability to generate solutions to a problem where a rigorous analytical solution do not exist, e.g., visibility forecasting. The framework was developed using GUIDE, a user interface design environment, and the Fuzzy Logic Toolbox of MATLAB.