Framework for Intersection Decision Support in Adverse Weather Conditions

The impact of inclement weather on driver behavior can be significant and may lead to erroneous driver decisions. Consequently, the research being presented used sensor information to assist opposed left-turning vehicles at signalized intersections under various weather conditions. The research proposed a new framework for a real-time intersection decision support system for left-turning vehicles entitled IDS-W. The proposed system was tested with data from a signalized intersection in Christiansburg, Virginia. The intersection was equipped with four video cameras and an on-site weather station. Nine thousand fifty-eight observations covering various weather conditions were gathered. Each observation consisted of the driver decision (accept or reject), weather condition (dry, rain, or snow), illumination (day or night), gap size, and gap-offered location (lane number). The observed decisions and the corresponding gap variables were aggregated to build the IDS-W database, which was developed by fusing data from roadside sensors (input data) and provided a decision (accept or reject the offered gap) to the driver. A case-based reasoning approach was used to provide the gap acceptance decision. The approach's cycle starts with the entering of a new case (the combination of the gap size and the corresponding independent variables) and ends with the proposed decision (accept or reject the gap). The model was developed with 80% of the data to deduce the appropriate decision (accept or reject) and validated with the remaining 20% of the data through a Monte Carlo simulation. The results demonstrate the potential for delay reductions at the signalized intersection.