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Hybrid time series forecasting methods for travel time prediction

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  • Serin, Faruk
  • Alisan, Yigit
  • Kece, Adnan

Abstract

Providing accurate information about travel time to passengers is important in public transportation. In this aspect, the travel time of buses between two consecutive stops can be handled as time series. Then, the future travel time can be predicted using time series forecasting methods. In this study, we propose a novel method with three-layer architecture to predict bus travel time between two stops. In the first layer of the proposed method, initial prediction is made by processing measured data. In the second layer, residuals are predicted in the specified depth. In the third layer, the final prediction is made by integrating the results of two previous layers with three different approach. The experiments were performed on the data, which were obtained from public transportation of Istanbul, using various time series forecasting methods in form of traditional and proposed architecture. The results show that proposed method outperforms traditional approach with approximately MAPE of 6.

Suggested Citation

  • Serin, Faruk & Alisan, Yigit & Kece, Adnan, 2021. "Hybrid time series forecasting methods for travel time prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    • Handle: RePEc:eee:phsmap:v:579:y:2021:i:c:s0378437121004076
    DOI: 10.1016/j.physa.2021.126134
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    Cited by:

    1. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K. & Cao, Shuhan, 2023. "A generative model for vehicular travel time distribution prediction considering spatial and temporal correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
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    3. Jin Kuang & Tse-Chen Chang & Chia-Wei Chu, 2022. "Research on Financial Early Warning Based on Combination Forecasting Model," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    4. Bharathi, Dhivya & Vanajakshi, Lelitha & Subramanian, Shankar C., 2022. "Spatio-temporal modelling and prediction of bus travel time using a higher-order traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).

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