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