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Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach

Author

Listed:
  • Quang Hoc Tran

    (Faculty of Civil Engineering, University of Transport and Communications, 3 Cau Giay Rd., Lang Thuong, Dong Da, Hanoi 10000, Vietnam)

  • Yao-Min Fang

    (Geographic Information Systems Research Center, Feng Chia University, 100 Wenhua Rd., Situn, Taichung 40724, Taiwan)

  • Tien-Yin Chou

    (Geographic Information Systems Research Center, Feng Chia University, 100 Wenhua Rd., Situn, Taichung 40724, Taiwan)

  • Thanh-Van Hoang

    (Geographic Information Systems Research Center, Feng Chia University, 100 Wenhua Rd., Situn, Taichung 40724, Taiwan)

  • Chun-Tse Wang

    (Geographic Information Systems Research Center, Feng Chia University, 100 Wenhua Rd., Situn, Taichung 40724, Taiwan)

  • Van Truong Vu

    (Institute of Techniques for Special Engineering, Le Quy Don Technical University, 236 Hoang Quoc Viet Rd., Co Nhue, Bac Tu Liem, Hanoi 10000, Vietnam)

  • Thi Lan Huong Ho

    (Faculty of Civil Engineering, University of Transport and Communications, 3 Cau Giay Rd., Lang Thuong, Dong Da, Hanoi 10000, Vietnam)

  • Quang Le

    (Faculty of Civil Engineering, University of Transport and Communications, 3 Cau Giay Rd., Lang Thuong, Dong Da, Hanoi 10000, Vietnam)

  • Mei-Hsin Chen

    (Geographic Information Systems Research Center, Feng Chia University, 100 Wenhua Rd., Situn, Taichung 40724, Taiwan)

Abstract

Traffic speed forecasting in the short term is one of the most critical parts of any intelligent transportation system (ITS). Accurate speed forecasting can support travelers’ route choices, traffic guidance, and traffic control. This study proposes a deep learning approach using long short-term memory (LSTM) network with tuning hyper-parameters to forecast short-term traffic speed on an arterial parallel multi-lane road in a developing country such as Vietnam. The challenge of mishandling the location data of vehicles on small and adjacent multi-lane roads will be addressed in this study. To test the accuracy of the proposed forecasting model, its application is illustrated using historical voyage GPS-monitored data on the Le Hong Phong urban arterial road in Haiphong city of Vietnam. The results indicate that in comparison with other models (e.g., traditional models and convolutional neural network), the best performance in terms of root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MDAE) is obtained by using the proposed model.

Suggested Citation

  • Quang Hoc Tran & Yao-Min Fang & Tien-Yin Chou & Thanh-Van Hoang & Chun-Tse Wang & Van Truong Vu & Thi Lan Huong Ho & Quang Le & Mei-Hsin Chen, 2022. "Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6351-:d:822127
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    References listed on IDEAS

    as
    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    2. Zhanguo Song & Yanyong Guo & Yao Wu & Jing Ma, 2019. "Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-19, June.
    3. William Lam & Y. Tang & K. Chan & Mei-Lam Tam, 2006. "Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census," Transportation, Springer, vol. 33(3), pages 291-310, May.
    4. Shaopeng Zhong & Daniel (Jian) Sun, 2022. "Analyzing Spatiotemporal Congestion Pattern on Urban Roads Based on Taxi GPS Data," Springer Books, in: Logic-Driven Traffic Big Data Analytics, chapter 0, pages 97-118, Springer.
    5. Billah, Baki & King, Maxwell L. & Snyder, Ralph D. & Koehler, Anne B., 2006. "Exponential smoothing model selection for forecasting," International Journal of Forecasting, Elsevier, vol. 22(2), pages 239-247.
    6. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    7. Muhammad Zahid & Yangzhou Chen & Arshad Jamal & Coulibaly Zie Mamadou, 2020. "Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach," Sustainability, MDPI, vol. 12(2), pages 1-19, January.
    8. Shaopeng Zhong & Daniel (Jian) Sun, 2022. "Logic-Driven Traffic Big Data Analytics," Springer Books, Springer, number 978-981-16-8016-8, September.
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