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Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data

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  • Li, Linchao
  • Zhu, Jiasong
  • Zhang, Hailong
  • Tan, Huachun
  • Du, Bowen
  • Ran, Bin

Abstract

Inferring travel modes of travelers in the city is important to transportation planning and infrastructure design. Based on the distribution of travel modes, transportation engineers could provide some proper strategies to reduce traffic congestion and air pollution. With advanced sensing techniques, it is possible to collect high-resolution GPS trajectory data of travelers and we can infer travel modes using some popular machine learning methods. One of the difficult tasks facing the application of machine learning especially deep learning in travel mode detection is the lack of large, labeled dataset, because to label the trajectory data is expensive and time-consuming. Moreover, samples of different travel modes are always unbalanced. Accordingly, in this paper, we take advantage of the generative model and the Convolutional Neural Networks (CNN) to develop a hybrid travel modes detection model using less labeled trajectory data. Our key contribution is the utilization of a generative adversarial network (GAN) to artificially create some training samples in such a way that it not only increases the required sample size but balances the dataset to improve the accuracy of the detection model. Furthermore, CNN is applied to extract deep features of trajectory data, and then to classify the travel modes. The results show that the highest accuracy (86.70%) can be achieved by the proposed model. In particular, the proposed method can improve the detection accuracy of bus and driving modes because it can solve the small sample size problem. Moreover, the large sample size can provide an opportunity to develop some advanced deep learning models in future studies.

Suggested Citation

  • Li, Linchao & Zhu, Jiasong & Zhang, Hailong & Tan, Huachun & Du, Bowen & Ran, Bin, 2020. "Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 282-292.
  • Handle: RePEc:eee:transa:v:136:y:2020:i:c:p:282-292
    DOI: 10.1016/j.tra.2020.04.005
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    References listed on IDEAS

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    1. Stopher, Peter R. & Greaves, Stephen P., 2007. "Household travel surveys: Where are we going?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(5), pages 367-381, June.
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    Cited by:

    1. Chen, Liao & Ma, Shoufeng & Li, Changlin & Yang, Yuance & Wei, Wei & Cui, Runbang, 2024. "A spatial–temporal graph-based AI model for truck loan default prediction using large-scale GPS trajectory data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    2. Hong, Ye & Stüdeli, Emanuel & Raubal, Martin, 2023. "Evaluating geospatial context information for travel mode detection," Journal of Transport Geography, Elsevier, vol. 113(C).
    3. Jiajia Zhang & Tao Feng & Harry Timmermans & Zhengkui Lin, 2023. "Improved imputation of rule sets in class association rule modeling: application to transportation mode choice," Transportation, Springer, vol. 50(1), pages 63-106, February.

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