Traffic flow balancing and public transportation co-optimization under critical node failures

Abstract

Addressing the issue of road network vulnerability caused by the failure of critical transportation nodes, this study focuses on traffic flow reconstruction and adaptive optimization of the public transportation system following a major bridge collapse. The study first constructs a traffic flow model for urban transportation networks based on graph theory and uses traffic balancing equations and segment capacity constraints to quantitatively assess the impact of core corridor failures on the distribution of traffic flow across the entire network. By introducing a shortest-path optimization objective function, the study minimizes commuting costs while satisfying physical topological constraints. Building on this foundation, the study further proposes data-driven optimization strategies for the public transit system. The K-means clustering algorithm is used to identify high-density clusters of urban transportation demand, and a normalization model based on traffic distribution ratios is established to achieve precise allocation of public transit resources. To quantitatively evaluate the optimization results, this section designed a multi-criteria weighted scoring system covering traffic satisfaction, improvements in commuting efficiency, and safety margins. Empirical analysis indicates that this coupled model can effectively identify and alleviate secondary congestion points caused by traffic shifts, with a traffic distribution accuracy rate of 92%. It provides a scientific computational framework for the resilient recovery of road networks and the allocation of public transport resources following sudden infrastructure failures in cities.