Hidden high-risky states identification from routine urban traffic

TL;DR

This paper introduces a novel method using maximum entropy models and energy landscape analysis to identify hidden high-risk states in urban traffic systems, aiding early risk detection and management.

Contribution

It presents a new approach to detect unseen high-risk states in complex urban traffic systems by constructing energy landscapes from dynamical data.

Findings

Identified hidden high-risk states in urban traffic.

Linked high-risk states to hazardous minima in energy landscape.

Provided a framework for early risk warning in complex systems.

Abstract

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.