Abstract
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, 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, both 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%.
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, 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, both 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%.
| Original language | English |
|---|---|
| Title of host publication | Mobile Computing, Applications, and Services : 7th International Conference, MobiCASE 2015, Berlin, Germany, November 12-13, 2015, Revised Selected Papers |
| Editors | Stephan Sigg , Petteri Nurmi, Flora Salim |
| Number of pages | 18 |
| Volume | 162 |
| Publisher | Springer |
| Publication date | 2015 |
| ISBN (Print) | 978-3-319-29002-7 |
| ISBN (Electronic) | 978-3-319-29003-4 |
| DOIs | |
| Publication status | Published - 2015 |
| Event | EAI EAI International Conference on Mobile Computing, Applications and Services - Berlin, Germany Duration: 12 Nov 2015 → 13 Nov 2015 Conference number: 7 |
Conference
| Conference | EAI EAI International Conference on Mobile Computing, Applications and Services |
|---|---|
| Number | 7 |
| Country/Territory | Germany |
| City | Berlin |
| Period | 12/11/2015 → 13/11/2015 |
| Series | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering |
|---|---|
| Volume | 162 |
| ISSN | 1867-8211 |
Keywords
- Transportation Mode Detection
- Mobile Sensing
- Indoor Positioning