Quantifying navigation complexity in transportation networks

TL;DR

This study analyzes large-scale subway trip data from Chinese cities to quantify real-world navigation complexity, revealing that actual navigation costs are lower than theoretical estimates and can be improved in subnetworks.

Contribution

It introduces a data-driven approach to measure navigation difficulty using empirical trip data and models routing behaviors, providing insights into urban transportation planning.

Findings

People tend to reuse a small set of routes.

Navigation information in subnetworks is much less than in the global network.

A linear relationship exists between empirical and theoretical search information.

Abstract

The complexity of navigation in cities has increased with the expansion of urban areas, creating challenging transportation problems that drive many studies on the navigability of networks. However, due to the lack of individual mobility data, large-scale empirical analysis of the wayfinder's real-world navigation is rare. Here, using 225 million subway trips from three major cities in China, we quantify navigation difficulty from an information perspective. Our results reveal that 1) people conserve a small number of repeatedly used routes, and 2) the navigation information in the subnetworks formed by those routes is much smaller than the theoretical value in the global network, suggesting that the decision cost for actual trips is significantly smaller than the theoretical upper limit found in previous studies. By modeling routing behaviors in growing networks, we show that while the global network becomes difficult to navigate, navigability can be improved in subnetworks. We further present a universal linear relationship between the empirical and theoretical search information, which allows the two metrics to predict each other. Our findings demonstrate how large-scale observations can quantify real-world navigation behaviors and aid in evaluating transportation planning.