IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v579y2021ics0378437121004076.html
   My bibliography  Save this article

Hybrid time series forecasting methods for travel time prediction

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121004076
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126134?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
    2. Hajirahimi, Zahra & Khashei, Mehdi, 2019. "Weighted sequential hybrid approaches for time series forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    3. Celik, Erkan & Bilisik, Ozge Nalan & Erdogan, Melike & Gumus, Alev Taskin & Baracli, Hayri, 2013. "An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 58(C), pages 28-51.
    4. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    5. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    6. Paulo S G de Mattos Neto & George D C Cavalcanti & Francisco Madeiro & Tiago A E Ferreira, 2015. "An Approach to Improve the Performance of PM Forecasters," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-23, September.
    7. Wudi Wei & Junjun Jiang & Hao Liang & Lian Gao & Bingyu Liang & Jiegang Huang & Ning Zang & Yanyan Liao & Jun Yu & Jingzhen Lai & Fengxiang Qin & Jinming Su & Li Ye & Hui Chen, 2016. "Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-13, June.
    8. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).
    2. Hala Aburas & Isam Shahrour & Carlo Giglio, 2024. "Route Planning under Mobility Restrictions in the Palestinian Territories," Sustainability, MDPI, vol. 16(2), pages 1-23, January.
    3. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K., 2024. "A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    4. 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.
    5. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. You-Shyang Chen & Arun Kumar Sangaiah & Yu-Pei Lin, 2024. "Hyperautomation on fuzzy data dredging on four advanced industrial forecasting models to support sustainable business management," Annals of Operations Research, Springer, vol. 342(1), pages 215-264, November.
    2. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    3. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
    4. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    5. Uddin, Gazi Salah & Tang, Ou & Sahamkhadam, Maziar & Taghizadeh-Hesary, Farhad & Yahya, Muhammad & Cerin, Pontus & Rehme, Jakob, 2021. "Analysis of Forecasting Models in an Electricity Market under Volatility," ADBI Working Papers 1212, Asian Development Bank Institute.
    6. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
    7. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    8. Lin, Zhibin & Vlachos, Ilias, 2018. "An advanced analytical framework for improving customer satisfaction: A case of air passengers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 185-195.
    9. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    10. Yang, Jun & Guo, Fang & Zhang, Min, 2017. "Optimal planning of swapping/charging station network with customer satisfaction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 174-197.
    11. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    12. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    13. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
    14. Elsinga, Boudewijn & van Sark, Wilfried G.J.H.M., 2017. "Short-term peer-to-peer solar forecasting in a network of photovoltaic systems," Applied Energy, Elsevier, vol. 206(C), pages 1464-1483.
    15. Liu, Xiangfei & Ren, Mifeng & Yang, Zhile & Yan, Gaowei & Guo, Yuanjun & Cheng, Lan & Wu, Chengke, 2022. "A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings," Energy, Elsevier, vol. 259(C).
    16. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
    17. Xiwen Cui & Xinyu Guan & Dongyu Wang & Dongxiao Niu & Xiaomin Xu, 2022. "Can China Meet Its 2030 Total Energy Consumption Target? Based on an RF-SSA-SVR-KDE Model," Energies, MDPI, vol. 15(16), pages 1-13, August.
    18. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2018. "A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods," Energies, MDPI, vol. 11(11), pages 1-19, October.
    19. Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60, February.
    20. Caputo, Antonio C. & Federici, Alessandro & Pelagagge, Pacifico M. & Salini, Paolo, 2023. "Offshore wind power system economic evaluation framework under aleatory and epistemic uncertainty," Applied Energy, Elsevier, vol. 350(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:579:y:2021:i:c:s0378437121004076. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.