Multiple-factor based sparse urban travel time prediction

Xinyan Zhu, Yaxin Fan, Faming Zhang, Xinyue Ye, Chen Chen, Han Yue

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.

Original languageEnglish (US)
Article number279
JournalApplied Sciences (Switzerland)
Issue number2
StatePublished - Feb 12 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


  • Artificial neural networks
  • Big probe vehicles data
  • Data sparsity
  • Link travel time prediction
  • Multi-factor influences
  • Spatiotemporal relationships


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