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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.
Ghadami et al. (Fri,) studied this question.