Trip mode recognition using smartphone sensor data under different sampling frequencies

Shen, Jian; Jiang, Haihang; Yang, Fei; Yao, Zhaohui

doi:10.3233/web-190409

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Machine learning methods are extensively used by previous researchers on TMR task. Single machine learning model includes SVM [10], decision tree [15], regression method. Ensemble learning model includes stacking, bagging and boosting.…”

Section: Pipelinementioning

confidence: 99%

Multiple Tree Model Integration for Transportation Mode Recognition

Ren

2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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The team RY presents a solution for Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge, which aims at differentiating eight transportation modes with mobile phone signal sensor data in this paper. This study first extracted a set of reasonable and discriminative features after data-preprocessing. Then, decision tree bagging, random forest, lightGBM are trained separately as basic models, whose predictions are integrated and afterward smoothed. The method gets 0.65 accuracy score on validation dataset. CCS CONCEPTS• Computing methodologies → Artificial intelligence; Simulation environments; • General and reference → Cross-computing tools and techniques.

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Section: Pipelinementioning

confidence: 99%

Multiple Tree Model Integration for Transportation Mode Recognition

Ren

2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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“…In the fifth paper [10], Jian Shen, Haihang Jiang, Fei Yang and Zhenxing Yao provided a hybrid model composed of the correlation ratio analysis and the Support Vector Machine (SVM) for trip mode recognition. The correlation ratio analysis algorithm is applied to determine the optimal time window of the input attributes so as to optimize the input parameters, and the SVM is applied to carry out the trip mode recognition for the whole trip.…”

mentioning

confidence: 99%

Special issue on big data for IoT cloud computing convergence

Hou

Xiao

2019

WEB

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Trip mode recognition using smartphone sensor data under different sampling frequencies

Cited by 2 publications

References 19 publications

Multiple Tree Model Integration for Transportation Mode Recognition

Multiple Tree Model Integration for Transportation Mode Recognition

Special issue on big data for IoT cloud computing convergence

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