# Unveiling Self-Organization and Emergent Phenomena in Urban Transportation Systems via Multilayer Network Analysis

## TL;DR

This study explores how ride-hailing and metro systems interact to form self-organized patterns in urban transportation without central planning.

## Contribution

The paper introduces a four-layer temporal network model and two new indicators to analyze emergent intermodal phenomena in urban transport.

## Key findings

- A spontaneously emergent intermodal phenomenon between ride-hailing and metro networks was revealed.
- Intermodal patterns can occur on non-working days with large and homogeneous demand cohorts.
- A time-varying multicentric hierarchical structure was identified in the metro network.

## Abstract

In the absence of system-wide planning and coordination, emerging mobility services have been integrated into urban transportation systems as independent network layers. Meanwhile, their interactions with traditional public transit give rise to complex self-organizing patterns in population mobility, manifested as coopetitive dynamics. To systematically analyze this phenomenon, this study constructs a four-layer temporal network—consisting of ride-hailing, metro, combined, and potential layers—based on a vectorized multilayer network model and inter-layer mapping relationships. An analytical framework is then developed using node strength, cosine similarity, and rich-club coefficients, along with two newly proposed indicators: the intermodal index and the node importance coefficient. The results reveal, for the first time, a spontaneously emergent intermodal phenomenon between ride-hailing and metro networks, manifested through both cross-day modal substitution and intra-day intermodal chains. The analysis further demonstrates that when sufficiently large and homogeneous demand cohorts are present, the phenomena can emerge even on non-working days. Based on the characteristics of this phenomenon, a method has been developed to identify intermodal nodes across different transport networks. Furthermore, the study uncovers a time-varying multicentric hierarchical structure within the metro network, characterized by small-scale core rich nodes and larger-scale secondary rich-node clusters. Overall, this study provides novel insights into the formation, coordination, and optimization of intermodal urban transport networks.