Hidden high-risk states identification from routine urban traffic

One of the core risk management tasks is to identify hidden high-risk states that may lead to system breakdown, which can provide valuable early warning knowledge. However, due to the high dimensionality and nonlinear interactions embedded in large-scale complex systems like urban traffic, it remains challenging to identify hidden high-risk states from huge system state space where over 99% of possible system states are not yet visited in empirical data. Based on the maximum entropy model, we infer the underlying interaction network from complicated dynamical processes of urban traffic and construct the system energy landscape. In this way, we can locate hidden high-risk states that may have never been observed from real data. These states can serve as risk signals with a high probability of entering hazardous minima in the energy landscape, which lead to huge recovery cost. Our findings might provide insights for complex system risk management.