Automatic identification of near-stationary traffic states based on the PELT changepoint detection

•Theoretically differentiate steady, stationary, and equilibrium states.•Propose a four-step method to identify near-stationary states.•Develop an automatic closed-loop algorithm to determine critical parameters.•Provide two verification methods to verify the validity of near-stationary states. The existence of stationary states during peak periods has been an underlying assumption of many studies on analysis, operations, control, and management of transportation networks. In Cassidy (1998), a method was proposed to manually identify near-stationary states by visually inspecting transformed curves of cumulative total vehicle counts and occupancies. Such near-stationary states are important for calibrating fundamental diagrams, identifying active bottlenecks and incidents, and quantifying capacity drop magnitudes. To the best of our knowledge, however, there lacks an automatic method that can be applied to efficiently identify near-stationary states from a large amount of data. In this study, we attempt to fill this gap. We start with definitions of steady, stationary, and equilibrium states and discuss their relations. Then we present a novel four-step method for automatically identifying near-stationary states from raw loop-detector data: first, the raw data are pre-processed to obtain healthy datasets, fill in missing values, and normalize averaged vehicle counts and occupancies to the same scale; second, daily time series are partitioned into multiple candidate intervals based on the pruned exact linear time (PELT) changepoint detection method; third, the characteristics of the candidate intervals are calculated; and finally, near-stationary states are selected based on modified Cassidy’s gap and duration criteria. We further close the loop by presenting an algorithm to automatically determine the penalty threshold in the second step and the gap threshold in fourth step to ensure the quantity as well as the quality of identified near-stationary states. In a case study, we apply the proposed method to identify near-stationary states from a large set of 30-s raw loop-detector data at a freeway mainline station. We verify the validity of identified near-stationary states both directly and indirectly. The results show that the identified near-stationary states are valid with high quality and the calibrated triangular fundamental diagram is well-fitted and physically meaningful. We finally conclude by discussing some future improvements and potential applica