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Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

Author

Listed:
  • William Lam
  • Y. Tang
  • K. Chan
  • Mei-Lam Tam

Abstract

The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region. Copyright Springer 2006

Suggested Citation

  • 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.
    • Handle: RePEc:kap:transp:v:33:y:2006:i:3:p:291-310
    DOI: 10.1007/s11116-005-0327-8
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    Citations

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    Cited by:

    1. Taylor, James W. & Snyder, Ralph D., 2012. "Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing," Omega, Elsevier, vol. 40(6), pages 748-757.
    2. Taylor, James W., 2010. "Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, Elsevier, vol. 26(4), pages 627-646, October.
    3. M. Milenković & N. Milosavljevic & N. Bojović & S. Val, 2021. "Container flow forecasting through neural networks based on metaheuristics," Operational Research, Springer, vol. 21(2), pages 965-997, June.
    4. Gu Pang & Bartosz Gebka, 2017. "Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2454-2469, May.
    5. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    6. Noelia Caceres & Luis M. Romero & Francisco J. Morales & Antonio Reyes & Francisco G. Benitez, 2018. "Estimating traffic volumes on intercity road locations using roadway attributes, socioeconomic features and other work-related activity characteristics," Transportation, Springer, vol. 45(5), pages 1449-1473, September.
    7. Ruiz-Aguilar, J.J. & Turias, I.J. & Jiménez-Come, M.J., 2014. "Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 1-13.
    8. 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.

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