An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Extracting Semantic Location from Outdoor Positioning Systems
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning and inferring transportation routines
Artificial Intelligence
Learning transportation mode from raw gps data for geographic applications on the web
Proceedings of the 17th international conference on World Wide Web
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Proceedings of the 6th ACM conference on Embedded network sensor systems
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Using mobile phones to determine transportation modes
ACM Transactions on Sensor Networks (TOSN)
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Mobility detection using everyday GSM traces
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Activity classification using realistic data from wearable sensors
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Manage and query generic moving objects in SECONDO
Proceedings of the VLDB Endowment
A hierarchical back-end architecture for smartphone sensing
Proceedings of the 2012 ACM Research in Applied Computation Symposium
GMOBench: a benchmark for generic moving objects
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
A generic data model for moving objects
Geoinformatica
Inferring human mobility patterns from taxicab location traces
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Accelerometer-based transportation mode detection on smartphones
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Using eye movements to recognize activities on cartographic maps
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
MoveSafe: a framework for transportation mode-based targeted alerting in disaster response
Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
An intelligent driver location system for smart parking
Expert Systems with Applications: An International Journal
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The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.