Dynamic bus arrival time prediction with artificial neural networks

Steven I.Jy Chien, Yuqing Ding, Chienhung Wei

研究成果: Article同行評審

304 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)429-438
期刊Journal of Transportation Engineering
出版狀態Published - 2002 9月 1

All Science Journal Classification (ASJC) codes

  • 土木與結構工程
  • 運輸


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