Understanding Road Usage Patterns in Urban Areas

TL;DR

This study utilizes large-scale mobile phone data and GIS information to reveal hidden patterns in urban road usage, emphasizing the importance of driver sources and network roles in traffic flow and system efficiency.

Contribution

It introduces a bipartite network framework for road usage, integrating driver source data with topological measures to better understand and optimize urban traffic.

Findings

Major road usage is driven by few key driver sources.

Road importance depends on betweenness and degree in the usage network.

Traffic reduction strategies based on driver source identification are effective.

Abstract

In this paper, we combine the most complete record of daily mobility, based on large-scale mobile phone data, with detailed Geographic Information System (GIS) data, uncovering previously hidden patterns in urban road usage. We find that the major usage of each road segment can be traced to its own - surprisingly few - driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach.