Understanding Human Perception of Bus Fullness: An Empirical Study of Crowdsourced Fullness Ratings and Automatic Passenger Count Data

Xidong Pi, Zhen (Sean) Qian, Aaron Steinfeld, Yun Huang

Research output: Contribution to journalArticlepeer-review


Bus fullness, namely bus crowding level, is one of the most critical metrics used to quantify public transportation service quality and consumer satisfaction. Often it is simply represented by the head count within a bus vehicle that is measured using the prevailing automatic passenger counter (APC)., Little is known, however, about how the precise passenger count reflects the human perception of bus fullness. This study examines the linkage between APC data and crowdsourced fullness ratings data from riders in Pittsburgh, U.S.A., to understand how riders’ perception of bus fullness is related to spatial, temporal, and demographic factors in addition to the passenger counts. We found that human perception of bus fullness, matched with passenger counts, can vary substantially by time of day, bus vehicle seat capacity, and passengers’ income. Last but not least, we proposed a statistical model that estimates riders’ perception of bus fullness based on APC data and bus route characteristics. This model can be used by transit agencies who possess APC data to assess service quality better, and ultimately to enhance the design and operation of transit systems.

Original languageEnglish (US)
Pages (from-to)475-484
Number of pages10
JournalTransportation Research Record
Issue number8
StatePublished - Dec 1 2018
Externally publishedYes

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

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