Understanding the mobility of a traveller from mobile sensor data is an important area of work in context aware and ubiquitous computing. Given a multimodal GPS trace, we will identify where in the GPS trace the traveller changed transportation modes. For example, where in the GPS trace the traveller alight a bus and boards a train, or where did the client stop running and start walking. Using data mining schemes to understand mobility data, in conjunction with real world observations, we propose an algorithm to identify mobility transfer points automatically. We compared the proposed algorithm against the state of the art that is used in the previously proposed work. Evaluation on real world data collected from GPS enabled mobile phones indicate that the proposed algorithm is accurate, has a good coverage, and a good asymptotic run time complexity.
Automated transportation transfer detection using GPS enabled smartphones
01.09.2012
947258 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Transportation Mode Detection by Using Smartphones and Smartwatches with Machine Learning
Springer Verlag | 2022
|Monitoring Air Quality Using an IoT-Enabled Air Pollution System on Smartphones
Springer Verlag | 2022
|