An improved model for predicting trip mode distribution using convolution deep learning


Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic, and air pollution. The majority of existing trip mode inference models operate based on human-selected features and traditional machine learning algorithms. However, human-selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

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  1. 1.

    Deakin E, Frick KT, Skabardonis A (2009) Intelligent transport systems

  2. 2.

    Stenneth L, Wolfson O, Yu PS, Xu B (2011) Transportation mode detection using mobile phones and GIS information. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2011, pp 54–63

  3. 3.

    Feng T, Timmermans HJ (2013) Transportation mode recognition using GPS and accelerometer data. Transp Res Part C Emerg Technol 37:118–130

    Article  Google Scholar 

  4. 4.

    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 

  5. 5.

    Xiao Z, Wang Y, Fu K, Wu F (2017) Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS Int J Geo-Inf 6(2):57

    Article  Google Scholar 

  6. 6.

    Zheng Y, Liu L, Wang L, Xie X (2008) Learning transportation mode from raw gps data for geographic applications on the web. In: Proceedings of the 17th International Conference on World Wide Web, 2008, pp 247–256

  7. 7.

    Sun Z, Ban XJ (2013) Vehicle classification using GPS data. Transp Res Part C Emerg Technol 37:102–117

    Article  Google Scholar 

  8. 8.

    Mäenpää H, Lobov A, Lastra JLM (2017) Travel mode estimation for multi-modal journey planner. Transp Res Part C Emerg Technol 82:273–289

    Article  Google Scholar 

  9. 9.

    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 

  10. 10.

    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 

  11. 11.

    Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. ArXiv Prepr. ArXiv150203167

  12. 12.

    Chollet F (2015) Keras

  13. 13.

    Zheng Y, Fu H, Xie X, Ma W-Y, Li Q (2011) Geolife GPS trajectory dataset—User Guide, Geolife GPS trajectories 1.1. 2011

  14. 14.

    Zheng Y, Xie X, Ma W-Y (2010) Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull 33(2):32–39

    Google Scholar 

  15. 15.

    Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. ArXiv Prepr. ArXiv14126980

  16. 16.

    Zheng Y, Li Q, Chen Y, Xie X, Ma W-Y (2008) Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, 2008, pp 312–321

  17. 17.

    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, 2016, pp 54–66

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Correspondence to Amin Nezarat.

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Nezarat, A., Seifadini, N. An improved model for predicting trip mode distribution using convolution deep learning. J Supercomput (2020).

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  • Deep learning
  • Convolutional neural network
  • Trip prediction