Stochastic Characteristics of Freeway Traffic Speed during Breakdown and Recovery Periods

In recent years, major urban freeways have been experiencing frequent and prolonged congestion throughout the day. Freeways, which are the primary source of mobility, are now operating at conditions near or beyond capacity. Such conditions typically lead to cyclic breakdown and recovery periods during which traffic undergoes dynamic transitions between steady state conditions and unsteady state conditions. Such transitions are probabilistic in nature in that breakdown and recovery events are not necessarily observed under the same conditions all the time. This study examines the basic stochastic characteristics of freeway traffic behavior under a wide range of traffic conditions, with emphasis on transient periods of breakdown and recovery. The study used 30-s speed data as an indicative measure of traffic conditions. Transition probabilities were estimated from real-world speed observations and categorized into negative or positive transitions. On the functions of negative and positive transition probability, five critical points were identified by their probability values (minimum, maximum, and a value of .5). The comparison of critical states at different lanes and locations showed similar characteristics in relation to the speed values at which those states were observed. For each transition, the expected magnitude of change (negative or positive) in speed was also calculated, and three states were identified on the expected transition curves. The stochastic characteristics revealed in this study could help traffic agencies gain a better understanding of the behavior of freeway traffic during breakdown and recovery periods when they are applying appropriate freeway management strategies such as demand packaging, ramp metering, and short-term traffic prediction, among others.