# Measuring and evolution analyzing of the reliability in urban public transport composite network

## TL;DR

This paper introduces a new way to measure reliability in urban public transport networks by combining connectivity and transport capacity metrics.

## Contribution

The study proposes a novel reliability indicator system and uses a CML model to analyze failure mechanisms in multimodal transport networks.

## Key findings

- Transport capacity reliability slightly rebounds when network damage exceeds 30%.
- Both reliability indicators approach zero when damage surpasses 50%.
- Early isolation of faulty nodes and recovery of high-betweenness nodes improve network efficiency.

## Abstract

With the expansion of urban public transport systems and the increasing integration of multiple modes, ensuring the reliability of composite public transport network has become a critical challenge. To overcome the limitations of traditional reliability research, which primarily relies on connec-tivity analysis, this paper proposes an innovative indicator system integrating connectivity reliability with a newly constructed transport capacity reliability dimension. The latter incorporates dynamic metrics such as passenger load and time delay to more comprehensively characterize actual operational conditions. By establishing a Coupled Map Lattice (CML) model to simulate the cascading propagation of node and line failures under multimodal coupling effects, this study reveals the failure mechanisms and recovery potential of complex networks under dynamic disturbances. Taking Changchun as a case study, the analysis reveals that both connectivity and transport capacity reliability experience minimal decline in the initial stages of disruption. When the scale of network damage exceeds 30%, transport capacity reliability shows a slight rebound. However, once the damage surpasses 50%, both indicators approach zero. The findings suggest that isolating faulty nodes in the early stages and prioritizing the recovery of high-betweenness nodes in the mid-stage can help maintain network operational efficiency. These results provide important theoretical guidance for enhancing the operational reliability and service quality of public transport systems. By applying complex network theory and data-driven methodologies, this research contributes to improving the resilience and sustainability of urban transport systems.