Name
Title | ||
|---|---|---|
A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection. |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18010087 | SENSORS |
Keywords | Field | DocType |
transport mode detection,post-processing,smartphone,accelerometer,gyroscope,magnetometer,correction of misclassified vehicle types,pedestrian and vehicular activities | Gyroscope,Architecture,Accelerometer,Electronic engineering,Artificial intelligence,Engineering,Statistical classification,Transportation planning,Machine learning | Journal |
Volume | Issue | Citations |
18 | 1.0 | 3 |
PageRank | References | Authors |
0.43 | 21 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Amaç Güvensan | 1 | 7 | 1.20 |
| Burak Dusun | 2 | 3 | 0.76 |
| Baris Can | 3 | 3 | 0.43 |
| H. Irem Türkmen | 4 | 20 | 3.93 |