Skip to main content

Semi-supervised GANs to Infer Travel Modes in GPS Trajectories

Abstract

This study experiments with the use of adversarial networks to classify travel mode based on one-dimensional smartphone trajectory data. We use data from a large-scale smartphone travel survey in Montreal, Canada. We convert GPS trajectories into fixed-sized segments with five channels (or variables). We develop different GANs architectures and compare their prediction results with convolutional neural networks (CNNs). The best semi-supervised GANs model led to a prediction accuracy of 83.4%, while the best CNN model was able to achieve the prediction accuracy of 81.3%. The results compare favorably with previous studies, especially when taking the large, real-world nature of the dataset into account. The developed semi-supervised GANs models share the same architectural innovations used in the image recognition literature, that we show can be used in travel information inference from smartphone travel survey data, not only to generate more labeled samples but also to improve the prediction performance of the classifier. Future work will allow exploration of better-performing models either with more channels and/or improved architectures.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  • Assemi B, Safi H, Mesbah M, Ferreira L (2016) Developing and validating a statistical model for travel mode identification on smartphones. IEEE Trans Intell Transp Syst 17(7):1920–1931

    Article  Google Scholar 

  • Bantis T, Haworth J (2017) Who you are is how you travel: a framework for transportation mode detection using individual and environmental characteristics. Transp Res Part C Emerg Technol 80:286–309

    Article  Google Scholar 

  • Chavdarova T, Fleuret F (2018) An alternative training of generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9407–9415

  • Dabiri S, Heaslip K (2018) Inferring transportation modes from GPS trajectories using a convolutional neural network. Transp Res Part C Emerg Technol 86:360–371

    Article  Google Scholar 

  • Dabiri S, Lu C, Heaslip K, Reddy CK (2020) Semi-supervised deep learning approach for transportation mode identification using GPS trajectory data. IEEE Trans Knowl Data Eng 32(5):1010–1023. https://doi.org/10.1109/TKDE.2019.2896985

    Article  Google Scholar 

  • Dalumpines R, Scott DM (2017) Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and python. Transp Plan Technol 40(5):523–539

    Article  Google Scholar 

  • Dumoulin V, Visin F (2016) A guide to convolution arithmetic for deep learning. arXiv: 1603.07285

  • Eftekhari HR, Ghatee M (2016) An inference engine for smartphones to preprocess data and detect stationary and transportation modes. Transp Res Part C Emerg Technol 69:313–327

    Article  Google Scholar 

  • Endo Y, Toda H, Nishida K, Kawanobe A (2016) Deep feature extraction from trajectories for transportation mode estimation. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp 54–66 (2016)

  • Girshick R, Iandola F, Darrell T, Malik J (2015) Deformable part models are convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 437–446

  • Gonzalez PA, Weinstein JS, Barbeau SJ, Labrador MA, Winters PL, Georggi NL, Perez R (2010) Automating mode detection for travel behaviour analysis by using global positioning systems-enabled mobile phones and neural networks. IET Intel Transp Syst 4(1):37–49

    Article  Google Scholar 

  • Goodfellow I (2016) Nips 2016 tutorial: generative adversarial networks. arXiv: 1701.00160

  • Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680

  • Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT press, Cambridge

    MATH  Google Scholar 

  • Gui J, Sun Z, Wen Y, Tao D, Ye J (2001) A review on generative adversarial networks: algorithms, theory, and applications. arXiv: 2001.06937

  • Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv: 1502.03167

  • Kalatian A, Farooq B (2018) Mobility mode detection using wifi signals. In: IEEE international smart cities conference

  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inform Process Syst 1:1097–1105

    Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  • Ledig C, Theis L, Husz´ar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681–4690

  • Li L, Zhu J, Zhang H, Tan H, Du B, Ran B (2020) Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data. Transportation Research Part A 136:282–292. https://doi.org/10.1016/j.tra.2020.04.005. http://www.sciencedirect.com/science/article/pii/S0965856420305607

  • Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML, vol 30, p 3

  • Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv: 1411.1784

  • Odena A (2016) Semi-supervised learning with generative adversarial networks. arXiv: 1606.01583

  • Patterson Z, Fitzsimmons K (2016) Datamobile: smartphone travel survey experiment. J Transp Res Board 2594:35–43

    Article  Google Scholar 

  • Pearson D (2004) A comparison of trip determination methods in GPS-enhanced household travel surveys. In: 84th annual meeting of the Transportation Research Board, Washington, DC

  • Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv: 1511.06434

  • Rezaie M, Patterson Z, Yu JY, Yazdizadeh A (2017) Semi-supervised travel mode detection from smartphone data. In: Smart Cities Conference (ISC2), 2017 International. IEEE, pp 1–8 (2017)

  • Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. In: Advances in neural information processing systems, pp 2234–2242

  • Shafique MA, Hato E (2016) Travel mode detection with varying smartphone data collection frequencies. Sensors 16(5):716

    Article  Google Scholar 

  • Song J, Ren H, Sadigh D, Ermon S (2018) Multi-agent generative adversarial imitation learning. Adv Neural Inform Process Syst 31:7461–7472

    Google Scholar 

  • Springenberg JT (2015) Unsupervised and semi-supervised learning with categorical gener- ative adversarial networks. arXiv: 1511.06390

  • Stopher PR (2009) The travel survey toolkit: where to from here? In: Transport survey methods: keeping up with a changing world. Emerald Group Publishing Limited, Bradford, pp 15–46 (2009)

  • Wang H, Liu G, Duan J, Zhang L (2017) Detecting transportation modes using deep neural network. IEICE Trans Inf Syst 100(5):1132–1135

    Article  Google Scholar 

  • Warde-Farley D, Goodfellow I (2016) 11 adversarial perturbations of deep neural networks. Perturbations, Optimization, and Statistics. https://doi.org/10.7551/mitpress/10761.003.0012

    Article  MATH  Google Scholar 

  • Wolf J, Oliveira M, Thompson M (2003) Impact of underreporting on mileage and travel time estimates: Results from global positioning system-enhanced household travel survey. J Transp Res Board 1854:189–198

    Article  Google Scholar 

  • Yazdizadeh A, Patterson Z, Farooq B (2018) An automated approach from GPS traces to complete trip information. Int J Transp Sci Technol 8:82–100

    Article  Google Scholar 

  • Zaki M, Sayed T, Shaaban K (2014) Use of drivers’ jerk profiles in computer vision-based traffic safety evaluations. J Trans Tion Res Board 2434:103–112

    Article  Google Scholar 

  • Zheng Y, Li Q, Chen Y, Xie X, Ma WY (2008) Understanding mobility based on GPS data. In: Proceedings of the 10th international conference on Ubiquitous computing. Association for Computing Machinery, New York, pp 312–321

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ali Yazdizadeh.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yazdizadeh, A., Patterson, Z. & Farooq, B. Semi-supervised GANs to Infer Travel Modes in GPS Trajectories. J. Big Data Anal. Transp. 3, 201–211 (2021). https://doi.org/10.1007/s42421-021-00047-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42421-021-00047-y

Keywords

  • Generative adversarial networks
  • Convolutional neural networks
  • GPS trajectories
  • Mode inference
  • Smartphone household travel survey