Effects of traffic network dynamics on hierarchical community-based representations of large road networks

Abstract

Efficient representation of traffic networks, including congestion states, plays an important role in the effectiveness of routing algorithms incorporating Intelligent Transportation Systems (ITS) data. We employ an emerging concept in analyzing complex networks called “community structure detection” to capture traffic network dynamics in the form of hierarchical community-based representations of road networks. A key strength of these community (structure) detection methods is their computational efficiency. We investigate the impact of traffic dynamics on the hierarchical community-based representations of large road networks. The resulting hierarchical community representations and their evolution over varying traffic conditions with time can aid the computational performance of real-time routing algorithms. We analyze the performance of hierarchical community detection methods on the metropolitan road networks of New York City, Detroit, and San Francisco Bay area.