Real-Time Detection of Crowded Buses via Mobile Phones
This addresses the need for cost-effective APC to improve rider experience and transit planning, but it is incremental as it extends existing accelerometer models to a new application.
The paper tackled the problem of automated passenger counting (APC) for public transit by using smartphone accelerometers to detect if riders are standing or sitting on buses, providing evidence that this method is a valid new data source for APC.
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.