This work uses regression models to analyze two characteristics of recurrent congestion:breakdown,the transition from freely flowing conditions to a congested state,and duration,the time between the onset and clearance of recurrent congestion.First,we apply a binary logistic regression model where a continuous measurement for traffic flow and a dichotomous categorical variable for time-of-day(AM-or PM-rush hours) is used to predict the probability of breakdown.Second,we apply an ordinary least squares regression model where categorical variables for time-of-day(AM-or PM-rush hours) and day-of-the-week(Monday-Thursday or Friday) are used to predict recurrent congestion duration.Models are fitted to data collected from a bottleneck on 1-93 in Salem,NH,over a period of 9 months.Results from the breakdown model,predict probabilities of recurrent congestion,are consistent with observed traffic and illustrate an upshift in breakdown probabilities between the AM-and PM-rush periods.Results from the regression model for congestion duration reveal the presences of significant interaction between time-of-day and day-of-the-week.Thus,the effect of time-of-day on congestion duration depends on the day-of-the-week.This work provides a simplification of recurrent congestion and recovery,very noisy processes.Simplification,conveying complex relationships with simple statistical summaries-facts,is a practical and powerful tool for traffic administrators to use in the decision-making process. This work uses regression models to analyze two characteristics of recurrent congestion: breakdown, the transition from freely flowing conditions to a congested state, and duration, the time between the onset and clearance of recurrent congestion. First, we apply a binary logistic regression model where a continuous measurement for traffic flow and a dichotomous categorical variable for time-of-day (AM-or PM-rush hours) is used to predict the probability of breakdown. Second, we apply an ordinary least squares regression model where categorical variables for time -of-day (AM-or PM-rush hours) and day-of-the-week (Monday-Thursday or Friday) are used to predict recurrent congestion duration. Models are fitted to data collected from a bottleneck on 1-93 in Salem, NH, over a period of 9 months. Results from the breakdown model, predict probabilities of recurrent congestion, are consistent with observed traffic and illustrate an upshift in breakdown probabilities between the AM-and PM-rush periods.Results fr om the regression model for congestion duration reveal the presences of significant interaction between time-of-day and day-of-the-week. Thus, the effect of time-of-day on congestion duration depends on the day-of-the- week. This work provides a simplification of recurrent congestion and recovery, very noisy processes. Simplification, conveying complex relationships with simple statistical summaries-facts, is a practical and powerful tool for traffic administrators to use in the decision-making process.