A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection


GÜVENSAN M. A. , DUSUN B., CAN B., TURKMEN H. İ.

SENSORS, cilt.18, 2018 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Cilt numarası: 18 Konu: 1
  • Basım Tarihi: 2018
  • Doi Numarası: 10.3390/s18010087
  • Dergi Adı: SENSORS

Özet

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.