Passenger Perceptions, Information Preferences, and Usability of Crowding Visualizations on Public Displays in Transit Stations and Vehicle

Title of the Paper

Passenger Perceptions, Information Preferences, and Usability of Crowding Visualizations on Public Displays in Transit Stations and Vehicles

Paper Information

• Subject Area: Human-Computer Interaction and Public Transit Crowding Visualization
• Keywords: Crowding Perception, Public Transit, Public Displays, Visualization, COVID-19

Research Background and Problem

• Problem or Challenge: Crowding in public transit stations and vehicles poses challenges for route navigation, health hygiene, and maintaining physical distancing. However, existing public displays often fail to effectively convey crowding information, making it difficult for passengers to make informed decisions.
• Significance: Especially in the post-COVID-19 era, people are more sensitive to hygiene and safety in public transit. Understanding crowding conditions can reduce health risks and improve passenger experience.
• Research Motivation and Related Work:
  • Existing studies mostly treat transit crowding as a comfort issue rather than a safety concern.
  • While some research has explored the importance of crowding information during the pandemic, there is limited investigation into the specific crowding information passengers need at different stages of their journeys.
  • Most current visualization studies are limited to conveying overall crowding levels and lack a comprehensive exploration of passenger needs.

Solution

• Research Methods:
  1. An online survey (303 North American public transit passengers) to explore changes in crowding perception and information preferences.
  2. Development of two crowding visualization prototypes based on historical passenger event data, tested through a field experiment (44 participants).
• Innovations:
  • Systematic study of changes in passengers' crowding perceptions before and during the pandemic.
  • Creation and testing of three types of crowding visualization concepts, including overall fullness, physical distancing, and specific occupancy locations.
  • Comparison of the utility differences between "simple and understandable" and "information-rich" visualization designs.
• Implementation Steps:
  1. Online Survey: Collect passenger background information, perceptions of crowding density, and preferences for crowding and health-related information.
  2. Prototype Design and Evaluation:
     • Fullness (overall fullness): A five-level scale resembling a "battery icon."
     • Distance (physical distancing): Displays physical spacing between passengers.
     • Occupancy (specific occupancy locations): Uses block icons to indicate occupied seats and standing areas.
  3. Field Testing: Simulate real-time crowding scenarios using historical data to further refine the Fullness and Occupancy designs and evaluate their usability in real-world environments.

Research Findings

• Specific Findings:
  • Passenger sensitivity to crowding significantly increased during the pandemic, with a density of approximately 2 people per square meter being perceived as unacceptable by most.
  • The Fullness design scored highest in comprehensibility (most simple and easy to read), while the Occupancy design was the most practical (providing detailed information that better supports boarding planning).
  • Displaying crowding information before train arrival and on the platform better supports passenger decision-making, while the need for such information decreases significantly after boarding.
• Advantages:
  • Addresses the trade-off between simple and understandable vs. information-rich visualization designs.
  • Improves passenger experience by helping avoid crowded areas, choose appropriate train doors, and select seats.
  • Encourages post-pandemic passengers to return to public transit.
• Experimental Evaluation Results:
  • Overall, passengers preferred the Occupancy visualization design (64% of participants) for its richer information and better support for planning boarding locations.
  • The Fullness design, being simpler and faster to interpret, was better suited for quick decision-making scenarios in public transit.
• Limitations and Future Directions:
  • Limitations:
    • The experiment relied on self-reported behaviors from participants, without capturing actual behavior changes.
    • The study was conducted in a mid-sized city, not covering larger or higher-density metro systems.
  • Future Directions:
    • Implement real-time crowding information transmission and study its impact on passengers' long-term behavior.
    • Explore how dynamic navigation systems can integrate crowding information to enhance passenger journey experiences.
    • Further investigate differences in crowding perception and tolerance based on passengers' psychological and cultural backgrounds.