Time-Varying Reliability Assessment of Urban Traffic Network Based on Dynamic Bayesian Network

With the advancement of urbanization, the complexity and fragility of urban traffic systems are becoming increasingly prominent. The reliability of urban road networks, characterized by their dynamic nature, multi-scale characteristics, and anti-interference capabilities, directly restricts the functional guarantee of urban traffic and the efficiency of emergency response. To address the limitations of existing road network connectivity reliability assessment methods in representing time dynamics and modeling failure correlation, this study proposes a road network reliability assessment method based on Dynamic Bayesian Network (DBN) by constructing a probabilistic reasoning model that integrates cascading failure characteristics. First, the connectivity reliability of the road network under random and targeted attack strategies was evaluated using Monte Carlo Simulation (MCS), revealing the impact of different attack strategies on network reliability. Subsequently, considering the time-dependent failure distribution of road sections and their interdependencies, a cascading failure mechanism was introduced to build a time-varying reliability assessment model based on DBN. The effectiveness of the proposed method was verified through a case study of a partial road network in Dalian. The results show that ignoring cascading effects can significantly overestimate the reliability of the road network, especially during peak traffic hours, where such deviations may mask the real paralysis risks of the network. In contrast, the method proposed in this study fully considers time dynamics and failure correlation, and can better capture the reliability of the road network under various dynamic conditions, providing a scientific basis for the resilience planning and emergency management of urban traffic systems.