  Automated passenger counting (APC) technology is central to many aspects of
the public transit experience. APC information informs public transit planners
about utilization in a public transit system and operations about dynamic
fluctuations in demand. Perhaps most importantly, APC information provides one
metric to the rider experience - standing during a long ride because of a
crowded vehicle is an unpleasant experience. Several technologies have been
successfully used for APC including light beam sensing and video image
analysis. However, these technologies are expensive and must be installed in
buses. In this paper, we analyze a new source of data using statistical models:
rider smartphone accelerometers. Smartphones are ubiquitous in society and
accelerometers have been shown to accurately model user states such as walking
and sitting. We extend these models to use accelerometers to detect if the
rider is standing or sitting on a bus. Standing riders are a signal that the
bus is crowded. This paper provides evidence that user smartphones are a valid
source of participatory sensing and thus a new source of automated passenger
counting data.
