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On Collecting High Quality Labeled Data for Automatic Transportation Mode Detection
Technical Paper
2019-01-0921
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
With the recent advancements in sensing and processing capabilities of consumer mobile devices (e.g., smartphone, tablet, etc.), they are becoming attractive choices for pervasive computing applications. Always-on monitoring of human movement patterns is one of those applications that has gained a lot of importance in the field of mobility and transportation research. Automatic detection of the current transportation mode (e.g., walking, biking, riding a shuttle, etc.) of a consumer using data from their smartphone sensors enables delivering of a number of customized services for multi-modal journey planning. Most accurate models for automatic mode detection are trained with supervised learning algorithms. In order to achieve high accuracy, the training datasets need to be sufficiently large, diverse, and correctly labeled. Specifically, the training data requires each type of mode data to be collected for a minimum duration that is necessary and sufficient for building high accuracy models. Collecting such data in an efficient manner is challenging because of the variability in the test subjects’ multi-modal journey patterns, e.g., using mostly private vehicles for commute, not sufficiently using rideshares, etc. In this paper, we describe a Design of Experiment (DoE) to efficiently collect supervised training dataset from user smartphones in a controlled environment. In this DoE, we asked the subjects to use a data logger app during a multi-modal trip designed around the Ford Dearborn campus with the right trip characteristics. The app persistently logged GPS and motion sensor data in the background and sent it to a remote Hadoop server. Location data was used to detect movement and enhance the quality of the data collection in real-time, e.g., the app paused data logging when the user is detected to be waiting between two transit modes.
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Authors
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Citation
Rao, P., Melcher, D., Mitra, P., and Rao, S., "On Collecting High Quality Labeled Data for Automatic Transportation Mode Detection," SAE Technical Paper 2019-01-0921, 2019, https://doi.org/10.4271/2019-01-0921.Data Sets - Support Documents
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References
- Sina , D. and Kevin , H. Inferring Transportation Modes from GPS Trajectories Using a Convolutional Neural Network Transportation Research. Part C, Emerging Technologies 360 371 2018 10.1016/j.trc.2017.11.021
- Philippe , N. , Peter , W. , Simon , B. , Norbert , B. et al. Supporting Large-Scale Travel Surveys with Smartphones - A Practical Approach Transportation Research. Part C, Emerging Technologies 43 2014 10.1016/j.trc.2013.11.005
- Kalatian , A. and Shafahi , Y. Travel Mode Detection Exploiting Cellular Network Data 2016 10.1051/matecconf/20168103008
- Thanos , B. and James , H. Who You Are Is How You Travel: A Framework for Transportation Mode Detection Using Individual and Environmental Characteristics Transportation Research Part C: Emerging Technologies 286 309 2017 10.1016/j.trc.2017.05.003
- Stenneth , L. , Wolfson , O. , Yu , P.S. , and Xu , B. Transportation Mode Detection using Mobile Phones and GIS Information ACM SIGSPATIAL GIS 2011
- Lin , L. , Donald , J.P. , Dieter , F. , and Henry , K. Learning and Inferring Transportation Routines Artificial Intelligence 171 311 331 2007
- Zheng , Y. , Li , Q. , Chen , Y. , Xie , X. et al. Understanding Mobility Based on GPS Data Ubiquitous Computing 312 321 2008
- Patterson , D. , Liao , L. , Fox , D. , and Kautz , H. Inferring High-Level Behavior from Low-Level Sensors ACM UBICOMP 2003
- Dongyoun , S. , Daniel , A. , Bige , T. , Stefan , M.A. et al. Urban Sensing: Using Smartphones for Transportation Mode Classification Computers, Environment and Urban Systems 53 76 86 2015
- Xia , H. , Qiao , Y. , Jian , J. , and Chang , Y. Using Smart Phone Sensors to Detect Transportation Modes Basel 14 2014 10.3390/s141120843
- Martin , B.D. , Addona , V. , Wolfson , J. , Adomavicius , G. et al. Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data Basel 17 2017 10.3390/s17092058
- Reddy , S. , Mun , M. , Burke , J. , Estrin , D. et al. Using Mobile Phones to Determine Transportation Modes ACM Transactions on Sensor Networks 6 2010
- Amac G , M. , Dusun , B. , Can , B. , and Turkmen , H.I. A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection Basel 18 2018 10.3390/s18010087
- Yu , M. , Yu , T. , Wang , S. , Lin , C. et al. Big Data, Small Footprint: The Design of a Low-Power Classifier for Detecting Transportation Modes Very Large Data Bases (VLDB) 2014
- https://www.modalyzer.com/en
- https://www.travelai.info/
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