Stochastic Model for Estimating Impact of Highway Incidents on Air Pollution and Traffic Delay

Abstract

A stochastic model was developed to estimate the average excess emission of carbon monoxide (CO), volatile organic compounds (VOC), oxides of nitrogen (NOx), and particulate matter (PM2.5) and the traffic delay due to incidents. This work models incident characteristics such as incident clearance time, degree of capacity reduction, and the demand-to-capacity ratio as random variables to derive the statistical characteristics of the excess emissions and traffic delays. It was found that estimated excess CO and traffic delay could be modeled as lognormal distributions. Excess VOC and NOx distributions were found to have the characteristics of a three-parameter lognormal distribution. Excess PM2.5 distribution was found to have gamma distribution characteristics. Average incident clearance time for this study was found to be 26 min. The average degree of capacity reduction and demand-to-capacity ratio were assumed to be 63% and 71%, respectively. An incident with these characteristics is estimated to result in 126.9 kg of excess CO, 20.8 kg of excess VOC, 8.8 kg of excess NOx, 0.27 kg of excess PM2.5 emissions, and 630 vehicle hours of traffic delay. This represents a 138% increase in CO emissions, a 500% increase in VOC emissions, a 26% increase in NOx emissions, and a 43% increase in PM2.5 emissions compared with the normal traffic emissions. Sensitivity analysis of incident management strategies revealed that air pollutant emissions and traffic delay could be reduced by as much as 30% by detouring as little as 5% of the incoming traffic.