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Inferencing transportation mode using unsupervised deep learning approach exploiting GPS point-level characteristics

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Abstract

Discovering the mode of transportation is a fundamental and challenging step in the various transportation analysis problems such as travel demand analysis, transport planning, and traffic management. Supervised learning approaches have frequently been used to detect the mode of transportation. In the supervised learning approach, extracted features from the GPS data and labeled mode of each trajectory are used to detect the mode of transportation. However, the sample size of trips for training is not enough due to the scarcity of labeled data. In real-time scenario, labeling the data is a tedious and expensive task. Furthermore, a huge amount of unlabeled data remains unutilized, which in turn degrades the quality of the solution. To overcome these drawbacks, an unsupervised learning method is proposed in this paper. In the proposed work, the point level characteristics such as speed, acceleration/deceleration, jerk, and bearing angle are fused with GPS coordinates to extract the joint probability densities using a type of generative deep learning model called masked autoregressive flow (MAF). Based on the probability density of each trajectory, K-means algorithm is applied to find the modes of transportation. The proposed model can handle the long sequence of trajectories having variable lengths. In this work, we compare our methodology with traditional machine learning algorithms and most related studies. The results are in favor of incorporating the proposed unsupervised approach in transportation mode detection.

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Data Availability

The datasets analyzed during the current study are available in the Geolife GPS trajectories 1.1 repository, https://www.microsoft.com/en-us/research/publication/geolife-gps-trajectory-dataset-user-guide/

Notes

  1. http://www.gov.cn/banshi/2005-08/23/content_25575.htm

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Acknowledgements

This work is partially funded by SERB (Science and Engineering Research Board, Government Of India) for project file no. EEQ/2018/001105 dated 12/02/2019.

Funding

This work is partially funded by SERB (Science and Engineering Research Board, Government Of India) for project file no. EEQ/2018/001105 dated 12/02/2019.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, 769008, Odisha, India

    Sumanto Dutta

  2. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India

    Bidyut Kr. Patra

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  1. Sumanto Dutta
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Sumanto Dutta: Conceptualization, Methodology, Software, Data Curation, Investigation, Writing - Original Draft, Bidyut Kr. Patra: Methodology, Supervision, Funding acquisition.

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Correspondence to Sumanto Dutta.

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Dutta, S., Patra, B.K. Inferencing transportation mode using unsupervised deep learning approach exploiting GPS point-level characteristics. Appl Intell 53, 12489–12503 (2023). https://doi.org/10.1007/s10489-022-04140-9

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