Landscape Geometry-based Percolation of Traffic in Several Populous Cities around the World

TL;DR

This paper introduces a landscape percolation model to analyze traffic congestion in populous cities, linking residential area size and road width, and validates it with global data, offering insights for urban planning.

Contribution

The study presents a novel landscape percolation approach to model traffic congestion, validated with real-world data, and provides practical guidelines for city infrastructure design.

Findings

The ratio of residential area size to road width controls congestion levels.

The model aligns well with data from Google Earth and traffic indices.

Insights can inform city planning and infrastructure redesign.

Abstract

We described the average traffic congestion in several populous cities around the world from a new concept, namely landscape percolation. The ratio of the residential area size to road width is a fundamental parameter that controls the traffic congestion. We have compared the model with data extracted from several populous cities around the world (directly from Google Earth images) and demonstrated very consistent results. The criterion for a city landscape that makes a city is considered as congested or less congested has been identified. The model also explains remarkably well the consistency of the measured data with various reports on congestion levels (such as the recognized Tomtom congestion level or Numbeo traffic index) of some populous cities around the world. These findings may help in designing new cities or redesigning the infrastructure of congested cities, for example for deciding what is the maximum size of the residential area and how width the roads are. This work also shows the similarity of the problem in conducting composite (electric current flow), brine transport between icebergs (fluid flow), and traffic (vehicle flow)