Investigating Effective Uncertainty Visualizations for Ordinal Crowdsourced Data of Crowding Conditions

2026-07-30

AI Summary

This study addresses the challenge of visualizing uncertainty in crowdsourced ordinal crowding data to support commuters in making better-informed travel decisions. Through an online user experiment, it presents the first systematic comparison of multiple visualization techniques—including cluster plots and bubble treemaps—in their ability to convey data variability and reliability. The study comprehensively evaluates how these visualizations influence users’ cognitive load, trust, and judgment accuracy. Results indicate that cluster plots significantly reduce cognitive load and enhance trust, whereas bubble treemaps yield higher accuracy in assessing crowding levels. These findings provide empirical evidence and actionable design guidance for visualizing uncertainty in crowding scenarios.

Abstract

Commuters often encounter crowding in railway systems, particularly in queues where passenger density varies throughout the day. This introduces uncertainty in crowdedness, making it difficult for individuals to anticipate conditions and plan their trips effectively. Crowdsourcing has been a valuable method for collecting localized user data. But the unpredictability of crowds and the uncertainty of crowdsourced information pose new challenges for decision-making. However, we know little about how to effectively visualize uncertainty in crowdedness to support informed commuting decisions, particularly when using crowdsourced ordinal data. Here, we investigated different uncertainty visualizations and their effectiveness in representing the variability and reliability of crowdsourced crowding data. They were evaluated through an online study, and we found that cluster visualization is best suited to reduce cognitive load while maximizing user confidence and trust. On the other hand, participants showed higher accuracy in determining crowd levels when using bubble treemaps.

Problem

• uncertainty visualization
• crowdsourced data
• ordinal data
• crowding conditions
• commuting decisions

Innovation

• uncertainty visualization
• crowdsourced ordinal data
• crowding conditions
• cluster visualization
• bubble treemaps