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Trip purpose inference for tourists by machine learning approaches based on mobile signaling data

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A Correction to this article was published on 23 August 2021

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Abstract

It has been gradually recognized that mobile phones can be used as a practical and promising way to identify individual travel trajectories. Researchers have developed various approaches to detecting human mobility and trip characteristics including trip origin–destination, travel modes, trip purposes based on mobile phone data. Among these researches, trip purpose detection has drawn less attention from researchers. This paper presents our work to investigate a set of machine learning approaches to identifying the trip purposes for tourists based on mobile signaling data combined with sampling surveys and point of interest (POI) data. Five machine learning algorithms, including support vector machine, decision tree, random forest, artificial neural network, and deep stacked auto-encoded (DSAE), have been employed to infer trip purposes under multiple scenarios. Four scenarios have been designed by considering the POI information around trip end [a 500 m buffer or Thiessen polygon (the coverage of the base station theoretically)] and training dataset selection (equal probabilities selection or equal proportion selection). The accuracy of trip purpose classification with machine learning algorithms has compared under different scenarios. The highest accuracy of 93.47% for the test dataset is achieved based on DSAE model under the scenario of a trip end 500 m buffer and equal probabilities selection. The experimental results indicate that the methodology developed with machine learning algorithms based on mobile signaling data combined with sample travel survey is expected as an alternative way to traditional travel surveys for trip purposes.

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Data availability statement

Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. (1) Mobile phone signaling data of Xiamen: This data is in cooperation with Xiamen communication operator, and our permission is only allowed to deploy the algorithm on their data platform and calculate the results. Meanwhile, the data cannot be token out. Therefore, this data is provided with restrictions. (2) POI data and survey data: These data are available from the corresponding author if requested. (3) Machine learning method: This related codes are also available from the corresponding author if requested.

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Acknowledgements

The authors would like to acknowledge the financial support for this study provided by the National Key Research and Development Plan of China (no. 2016YFE0206800) and Science and Technology Project of Beijing (no. Z181100003918011). Meanwhile, we would like to thank Prof. Jianming Ma at Texas Department of Transportation for his great comments and assistance on our experiments.

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Authors and Affiliations

  1. Beijing University of Technology, Beijing, 100124, China

    Haodong Sun, Yanyan Chen, Yang Wang & Xiaoming Liu

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  1. Haodong Sun
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Contributions

YC: Conception of the study and Constructive discussions. HS: Data analyses, Literature Search and Review, Manuscript Writing. YW: Manuscript editing and Constructive discussions. XL: Constructive suggestions.

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Correspondence to Yanyan Chen.

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Sun, H., Chen, Y., Wang, Y. et al. Trip purpose inference for tourists by machine learning approaches based on mobile signaling data. J Ambient Intell Human Comput 14, 923–937 (2023). https://doi.org/10.1007/s12652-021-03346-y

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