期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 22, 期 2, 页码 1093-1103出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2962741
关键词
Trajectory; Global Positioning System; Feature extraction; Data mining; Discrete wavelet transforms; Deep learning; Travel mode identification; GPS trajectory; discrete wavelet transform; deep learning; feature selection
资金
- General Program of Guangdong Basic and Applied Basic Research Foundation [2019A1515011032]
A travel mode identification mechanism based on discrete wavelet transform and recent developments of deep learning techniques is proposed in this study. It accurately identifies travel modes in both global and online scenarios and significantly outperforms existing methods in performance. Through comprehensive case studies and architecture testing, the effectiveness and efficiency of the mechanism are demonstrated.
Accurate identification in public travel modes is an essential task in intelligent transportation systems. In recent years, GPS-based identification is gradually replacing the conventional survey-based information-gathering process due to the more detailed and precise data on individual's travel patterns. Nonetheless, existing research suffers from deficient feature selection, high data dimensionality, and data under-utilization issues. In this work, we propose a novel travel mode identification mechanism based on discrete wavelet transform and recent developments of deep learning techniques. The proposed mechanism aims to take GPS trajectories of arbitrary lengths to develop accurate travel mode results in both global and online identification scenarios. In this mechanism, raw GPS data is first pre-processed to compute preliminary motion and displacement attributes, which are input into a tailor-made deep neural network. Discrete wavelet transform is also adopted to further extract time-frequency domain characteristics of the trajectories to assist the neural network in the classification task. To evaluate the performance of the proposed mechanism, a series of comprehensive case studies are conducted. The results indicate that the mechanism can notably outperform existing travel mode identifications on a same data set with minuscule computation time. Furthermore, an architecture test is performed to determine the best-performing structure for the proposed mechanism. Lastly, we demonstrate the capability of the mechanism in handling online identifications, and the performance sensitivity of the selected attributes is evaluated.
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