The efficient, reliable, and sustainable nature of a transportation system is a prerequisite to support the development of urban agglomeration. This paper proposes network modeling and resilience assessment methods for public transportation in urban agglomerations. A multi-layer network is constructed. With the identification of the key nodes in a multi-modal transportation network (MMTN), a resilience assessment method is proposed that considers two phases: absorption and recovery after an attack. The Beijing-Tianjin-Hebei urban agglomeration network is taken as a case study. The results show that the attack on key nodes brings more influence to MMTN than random attacks. More attention is suggested to be paid to the larger hub-type stations in operation and management. The proposed method can be applied in different types of urban agglomerations and serve as technical support for reducing the disorder and imbalance of MMTN.

Effective traffic management and congestion reduction heavily rely on accurate traffic flow prediction. Existing prediction methods, such as Markov, ARIMA, STANN, GLSTM, and DCRNN models, often face challenges because they rely on fixed spatial relationships, leading to limited long-term prediction accuracy. To address these shortcomings, this study proposes the Impedance-Spatio-Temporal Topological Network (Impedance-STTN) prediction model. The Impedance-STTN model integrates K-medoids clustering for data analysis, generating a real-time impedance matrix from impedance functions, traffic big data, and real-time flow data. This approach captures dynamic node relationships within the spatio-temporal network, enhancing prediction accuracy. Experimental results demonstrate the superior predictive performance of the Impedance-STTN model, achieving accuracies of 94.79%, 93.78%, and 93.11% in 5 min, 15 min, and 30 min predictions, respectively. These results outperform existing models, especially in long-term predictions. The findings underscore the model's high accuracy and effectiveness across varying prediction durations, marking a significant advancement in traffic flow prediction. This suggests promising avenues for future research and practical applications.