1. A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection.
- Author
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Guvensan, M. Amac, Dusun, Burak, Can, Baris, and Turkmen, H. Irem
- Subjects
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TRANSPORTATION planning , *CLASSIFICATION algorithms , *CHOICE of transportation , *MACHINE learning , *SMARTPHONES - Abstract
Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people's daily activities including transportation types and duration by taking advantage of individual's smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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