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

要旨

Large crowds in public transit stations and vehicles introduce obstacles for wayfinding, hygiene, and physical distancing. Public displays that currently provide on-site transit information could also provide critical crowdedness information. Therefore, we examined people's crowd perceptions and information preferences before and during the pandemic, and designs for visualizing crowdedness to passengers. We first report survey results with public transit users (n = 303), including the usability results of three crowdedness visualization concepts. Then, we present two animated crowd simulations on public displays that we evaluated in a field study (n = 44). We found that passengers react very positively to crowding information, especially before boarding a vehicle. Visualizing the exact physical spaces occupied on transit vehicles was most useful for avoiding crowded areas. However, visualizing the overall fullness of vehicles was the easiest to understand. We discuss design implications for communicating crowding information to support decision-making and promote a sense of safety.

著者
Leah Zhang-Kennedy
University of Waterloo, Waterloo, Ontario, Canada
Saira Aziz
University of Waterloo, Waterloo, Ontario, Canada
Oluwafunminitemi (Temi) Oluwadare
University of Waterloo, Waterloo, Ontario, Canada
Lyndon Pan
University of Waterloo, Waterloo, Ontario, Canada
Zeyu Wu
University of Waterloo, Waterloo, Ontario, Canada
Sydney E.C.. Lamorea
University of Waterloo, Waterloo, Ontario, Canada
Soda Li
University of Waterloo, Waterloo, Ontario, Canada
Michael Sun
University of Waterloo, University of Waterloo, Ontario, Canada
Ville Mäkelä
University of Waterloo, Waterloo, Ontario, Canada
論文URL

https://doi.org/10.1145/3544548.3581241

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Transportation and AI/ML

Hall G1
5 件の発表
2023-04-26 18:00:00
2023-04-26 19:30:00