Stability and Resilience of Transportation Systems: Is a Traffic Jam About to Occur?

Abstract

Measurement of traffic flow stability and resilience is a critical step toward evaluating the performance of transportation systems and implementing appropriate management strategies. Quantifying changes in the stability and resilience of transportation systems, however, is hampered by the complexity of real traffic dynamics and the diversity of infrastructures. Here, we demonstrate that changes in traffic flow stability and resilience are signaled by generic features, known as early warning signals in the theory of critical slowing down, observed before traffic instabilities occur. This finding is incorporated in an operational data-driven algorithm to evaluate the risk of traffic jams on highways. Theoretical findings and tests on simulated and empirical case studies support the premise of this approach and identify candidate statistical measures that are sensitive to changes in the stability and resilience of transportation systems. Our use of universal measures advances the monitoring capability, prediction and control of complex transportation systems.

Data-driven prediction in dynamical systems: recent developments

“…Data-driven prediction methods have also offered a solution to the formidable challenge of predicting catastrophic events in a variety of complex systems. Recent studies have shown that features extracted from data can be used to predict critical transitions [165] and extreme events [166] in the dynamics of a variety of complex systems, including aeroelastic systems [39,40,44,167,168], ecological systems [51,[169][170][171][172][173][174][175], epidemiological systems [176][177][178][179], traffic flow systems [158,180] and fluid flows [166,[181][182][183].…”

Stability Analysis for Urban Traffic Evolution Process Using Temporal Traffic State Patterns

“…Recurrent congestion has temporal periodicity and high spatial similarity, which is caused by insufficient infrastructure supply (An et al 2016). Previous studies have confirmed that traffic congestion is the result of instability and phase transition in the traffic flow dynamics (Ghadami et al 2022). To analyse the characteristics of traffic congestion, the traffic flow is abstracted into TSP (Lan et al 2008).…”