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

Transportation managers and engineers are often required to make decisions regarding the use of limited resources that directly affect public safety, costs, and the overall performance of transportation systems. One important decision involves prioritizing the deployment of resources (e.g., personnel and variable signs) to low visibility areas due to fog or other environmental and road conditions. Due to the lack of proper approaches to characterize and incorporate weather and road condition parameters in the decision-making process, transportation managers depend mainly on personal experience to make these types of decisions. To help them prioritize the deployment of resources to low visibility areas, this research presents a fuzzy inference system (FIS) framework composed of three fuzzy systems that characterize fog occurrence, road risk conditions, and deployment of resources. Preliminary experiments to evaluate the developed fog occurrence FIS against four methods presented in the literature using data from two weather stations showed that the FIS model outperformed three of the other methods in accuracy. These results are very promising given that the other methods represent more expensive solution approaches that require large amounts of data, significant time-consuming data preparation, network architecture design tasks, and high processing power.