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Regime shifts are quite common in complex systems like cell regulations,disease transmissions, ecosystems, marine ice instability, etc. Severalstatistical indicators known as early warning signals (EWS) have been theorizedto anticipate these abrupt transitions in advance. These regime shifts happenbecause they cross some critical value of the parameter that influences theoverall dynamics. This critical threshold is known as tipping point. In thevicinity of a tipping point, perturbations gradually increases, and as aconsequence, system-state extensively swing around the quasi-static attractor,and the local dynamics become progressively slow, which is known as criticalslowing down (CSD). Because of this CSD, statistical measures known as earlywarning signals (EWS) such as variance and lag-1 autocorrelation increase. Fromthe point of view of physics, a free flow can become congested when the meancar density crosses its tipping point. Recently, for lane-based traffic systemusing continuum model, study reveals that analysis of the generic EWSs serve asa good measure to predict upstream stop-and-go traffic jams. Now, we introduceEWSs to anticipate traffic jam for heterogeneous disordered traffic relevantfor non-lane-based systems. We have analyzed a lattice hydrodynamic areaoccupancy model with passing and through numerical simulations, we have shownemergence of kink or chaotic jam. Also, we provided proper framework forprediction of traffic jams via different EWSs. From simulated data, wedemonstrated that EWSs are sensitive as tipping is approached.

Anticipating Tipping Points for Disordered Traffic: Critical Slowing Down on the Onset of Congestion