Anticipating Tipping Points for Disordered Traffic: Critical Slowing Down on the Onset of Congestion (2401.09364v1)

Abstract: Regime shifts are quite common in complex systems like cell regulations, disease transmissions, ecosystems, marine ice instability, etc. Several statistical indicators known as early warning signals (EWS) have been theorized to anticipate these abrupt transitions in advance. These regime shifts happen because they cross some critical value of the parameter that influences the overall dynamics. This critical threshold is known as tipping point. In the vicinity of a tipping point, perturbations gradually increases, and as a consequence, system-state extensively swing around the quasi-static attractor, and the local dynamics become progressively slow, which is known as critical slowing down (CSD). Because of this CSD, statistical measures known as early warning signals (EWS) such as variance and lag-1 autocorrelation increase. From the point of view of physics, a free flow can become congested when the mean car density crosses its tipping point. Recently, for lane-based traffic system using continuum model, study reveals that analysis of the generic EWSs serve as a good measure to predict upstream stop-and-go traffic jams. Now, we introduce EWSs to anticipate traffic jam for heterogeneous disordered traffic relevant for non-lane-based systems. We have analyzed a lattice hydrodynamic area occupancy model with passing and through numerical simulations, we have shown emergence of kink or chaotic jam. Also, we provided proper framework for prediction of traffic jams via different EWSs. From simulated data, we demonstrated that EWSs are sensitive as tipping is approached.