Abstract
Transportation mode detection (TMD) is a growing field of research, in which a variety of methods have been developed, foremost for outdoor travels. It has been employed in application areas such as public transportation and environmental footprint profiling. For indoor travels the problem of TMD has received comparatively little attention, even though diverse transportation modes, such as biking and electric vehicles, are used indoors. The potential applications are diverse, and include scheduling and progress tracking for mobile workers, and management of vehicular resources. However, for indoor TMD, the physical environment as well as the availability and reliability of sensing resources differ drastically from outdoor scenarios. Therefore, many of the methods developed for outdoor TMD cannot be easily and reliably applied indoors.
In this paper, we explore indoor transportation scenarios to arrive at a conceptual model of indoor transportation modes, and then compare challenges for outdoor and indoor TMD. In addition, we explore methods for TMD we deem suitable in indoor settings, and we perform an extensive real-world evaluation of such methods at a large hospital complex. The evaluation utilizes Wi-Fi and accelerometer data collected through smartphones carried by hospital workers throughout four days of work routines. The results show that the methods can distinguish between six common modes of transportation used by the hospital workers with an F-score of \(84.2\,\%\).
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Notes
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In fact, these disturbances are sufficiently significant that they give rise to positioning via fingerprinting instead: given a magnetic field fingerprint collection, a phone’s location can subsequently be estimated within the fingerprinted environment by the local characteristics of the magnetic field as measured by the phone’s magnetometer [1].
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Prentow, T.S., Blunck, H., Kjærgaard, M.B., Stisen, A. (2015). Towards Indoor Transportation Mode Detection Using Mobile Sensing. In: Sigg, S., Nurmi, P., Salim, F. (eds) Mobile Computing, Applications, and Services. MobiCASE 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 162. Springer, Cham. https://doi.org/10.1007/978-3-319-29003-4_15
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DOI: https://doi.org/10.1007/978-3-319-29003-4_15
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