ESTIMATING MAGNITUDE AND DURATION OF INCIDENT DELAYS

Traffic congestion is a major operational problem on urban freeways. I n the case of recurring congestion, travelers can plan their trips acc ording to the expected occurrence and severity of recurring congestion . However, nonrecurring congestion cannot be managed without real-time prediction. Evaluating the efficiency of intelligent transportation s ystems (ITS) technologies in reducing incident effects requires develo ping models that can accurately predict incident duration along with t he magnitude of nonrecurring congestion. This paper provides two stati stical models for estimating incident delay and a model for predicting incident duration. The incident delay models showed that up to 85% of variation in incident delay can be explained by incident duration, nu mber of lanes affected, number of vehicles involved, and traffic deman d before the incident. The incident duration prediction model showed t hat 81% of variation in incident duration can be predicted by number o f lanes affected, number of vehicles involved, truck involvement, time of day, police response time, and weather condition. These findings h ave implications for on-line applications within the context of advanc ed traveler information systems (ATIS).