Towards Indoor Transportation Mode Detection Using Mobile Sensing

Transportation mode detection is a growing field of research, in which a variety of methods have been developed for detecting transportation modes foremost for outdoor travels. It has been employed in application areas such as public transportation, environmental footprint profiling, and context-aware mobile assistants. For indoor travels the problem of transportation mode detection has received comparatively little attention, even though diverse transportation modes, such as biking, electric vehicles, and scooters, are used indoors, especially in large building complexes. The potential applications are diverse, and may also extend beyond indoor variants of the above outdoor applications, and include, e.g., scheduling and progress tracking for mobile workers, management of vehicular resources, and navigation support. However, for indoor transportation mode detection, the physical environment as well as the availability and reliability of sensing resources differ drastically from outdoor scenarios. Owing to these differences, many of the methods developed for outdoor transportation mode detection 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 transportation mode detection. In addition, we explore methods for transportation mode detection we deem suitable in indoor settings, and we perform an extensive real-world evaluation of (combinations of) such methods at a large hospital complex. The evaluation presented here utilizes Wi-Fi and accelerometer data collected through smartphones carried by several 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%.