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

Chao Chen, Hui Wang, Fang Yuan, Huizhong Jia, Baozhen Yao (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

23 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)10435–10449
Number of pages15
JournalNeural Computing and Applications
Issue number14
Early online date2 Nov 2019
Publication statusPublished - 1 Jul 2020


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


Dive into the research topics of 'Bus travel time prediction based on deep belief network with back-propagation'. Together they form a unique fingerprint.

Cite this