Bus travel time prediction based on deep belief network with back-propagation

  • Chao Chen
  • Hui Wang
  • Fang Yuan
  • Huizhong Jia
  • Baozhen YaoEmail author
Original Article


In an intelligent transportation system, accurate bus information is vital for passengers to schedule their departure time and make reasonable route choice. In this paper, an improved deep belief network (DBN) is proposed to predict the bus travel time. By using Gaussian–Bernoulli restricted Boltzmann machines to construct a DBN, we update the classical DBN to model continuous data. In addition, a back-propagation (BP) neural network is further applied to improve the performance. Based on the real traffic data collected in Shenyang, China, several experiments are conducted to validate the technique. Comparison with typical forecasting methods such as k-nearest neighbor algorithm (k-NN), artificial neural network (ANN), support vector machine (SVM) and random forests (RFs) shows that the proposed method is applicable to the prediction of bus travel time and works better than traditional methods.


Bus travel time prediction Multi-factor influence Deep belief network Machine learning models 



This work was supported in National Natural Science Foundation of China (U1811463 and 51578112), The State Key Laboratory of Structural Analysis for Industrial Equipment (S18307). Finally, the authors gratefully acknowledge financial support from China Scholarship Council.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Structural Analysis for Industrial Equipment, School of Automotive EngineeringDalian University of TechnologyDalianPeople’s Republic of China
  2. 2.Urban Planning Group, Department of the Built EnvironmentEindhoven University of TechnologyEindhovenThe Netherlands

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