USING RELATIVE RISK ANALYSIS TO IMPROVE CONNECTICUT FREEWAY TRAFFIC SAFETY UNDER ADVERSE WEATHER CONDITIONS

This report conducts an investigation of a road accident prevention method using Relative Risk Ratio (RRR) analysis, considering the impact of combined weather and hourly traffic flow rate conditions. RRR is used to explore the relationship between accident risk and the combined situation of adverse weather, represented as rain and snow conditions, and hourly traffic flow rate. The findings show that accident risk under adverse weather is higher than under non-precipitation weather, and the highest accident risk occurs under snow conditions. To further evaluate the influence of traffic flow on weather-related accident risk, the authors adopted a method that considers capacity reduction under adverse weather to help explain the difference in accident risk. Exponential regression was used to build RRR models, primarily using V/C ratio as the explanatory variable. The results show that the rate of accidents observed in non-precipitation conditions would be maintained under precipitation conditions if the traffic volume could be reduced according to the reduced capacity.