Dynamic bus arrival time prediction with artificial neural networks

Steven I.Jy Chien, Yuqing Ding, Chienhung Wei

Research output: Contribution to journalArticlepeer-review

304 Scopus citations


Transit operations are interrupted frequently by stochastic variations in traffic and ridership conditions that deteriorate schedule or headway adherence and thus lengthen passenger wait times. Providing passengers with accurate vehicle arrival information through advanced traveler information systems is vital to reducing wait time. Two artificial neural networks (ANNs), trained by link-based and stop-based data, are applied to predict transit arrival times. To improve prediction accuracy, both are integrated with an adaptive algorithm to adapt to the prediction error in real time. The bus arrival times predicted by the ANNs are assessed with the microscopic simulation model CORSIM, which has been calibrated and validated with real-world data collected from route number 39 of the New Jersey Transit Corporation. Results show that the enhanced ANNs outperform the ones without integration of the adaptive algorithm.

Original languageEnglish (US)
Pages (from-to)429-438
Number of pages10
JournalJournal of Transportation Engineering
Issue number5
StatePublished - Sep 2002

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Transportation


  • Buses
  • Neural networks
  • Predictions
  • Simulation
  • Travel time


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