BAI, Lu, IRESON, Neil, MAZUMDAR, Suvodeep and CIRAVEGNA, Fabio (2017). Lessons learned using wi-fi and Bluetooth as means to monitor public service usage. In: LEE, Seungyon, TAKAYAMA, Leila and TRUONG, Khai, (eds.) Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. New York, ACM, 432-440. [Book Section]
Abstract
Facets of urban public transport such as occupancy,
waiting times, route preferences are essential to help
deliver improved services as well as better information
for passengers to plan their daily travel. The ability to
automatically estimate passenger occupancy in near
real-time throughout cities will be a step change in the
way public service usage is currently estimated and
provide significant insights to decision makers. The
ever-increasing popularity and abundance of mobile
devices with always-on Wi-Fi/Bluetooth interfaces
makes Wi-Fi/Bluetooth sensing a promising approach
for estimating passenger load. In this paper, we
present a Wi-Fi/Bluetooth sensing system to detect
mobile devices for estimating passenger counts using
public transport. We present our findings on an initial
set of experiments on a series of bus/tram journeys
encapsulating different scenarios over five days in a UK
metropolitan area. Our initial experiments show
promising results and we present our plans for future
large-scale experiments.
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