@inproceedings{1,
  keywords = {Crowding, Transportation, Public Displays, Visualization, Covid-19},
  author = {Leah Zhang-Kennedy and Saira Aziz and Oluwafunminitemi Oluwadare and Lyndon Pan and Zeyu Wu and Sydney Lamorea and Soda Li and Michael Sun and Ville Mäkelä},
  title = {Passenger Perceptions, Information Preferences, and Usability of Crowding Visualizations on Public Displays in Transit Stations and Vehicles},
  abstract = {<p>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.</p>
},
  year = {2023},
  journal = {CHI Conference on Human Factors in Computing Systems},
  publisher = {Association for Computing Machinery},
  url = {https://dl.acm.org/doi/abs/10.1145/3544548.3581241},
  doi = {10.1145/3544548.3581241},
}