Early Detection of a Traffic Flow Breakdown in the Freeway Based on Dynamical Network Markers

This paper presents a novel model-free method based on the dynamical network markers (DNM) to detect the traffic flow breakdown at an early transition stage in the context of the freeway connected with an on-ramp under a connected vehicle environment. In this method, the vehicle states are frequently observed at several cells or segments on each lane. By processing the observed data, the standard deviations and the correlation coefficients among the cells are analyzed to determine the dominant cells, the ones that are mostly influenced during the transition. Finally, the standard deviations and absolute values of the correlation coefficients of the dominant cells are combined to form a scalar warning signal, which provides a very strong indication when the traffic is at the critical state. The proposed method is evaluated through simulation on freeway traffic, whose flows are disturbed by the on-ramp merging vehicles.