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Short‐term traffic forecasting: Overview of objectives and methods

Author

Listed:
  • Eleni I. Vlahogianni
  • John C. Golias
  • Matthew G. Karlaftis

Abstract

In the last two decades, the growing need for short‐term prediction of traffic parameters embedded in a real‐time intelligent transportation systems environment has led to the development of a vast number of forecasting algorithms. Despite this, there is still not a clear view about the various requirements involved in modelling. This field of research was examined by disaggregating the process of developing short‐term traffic forecasting algorithms into three essential clusters: the determination of the scope, the conceptual process of specifying the output and the process of modelling, which includes several decisions concerning the selection of the proper methodological approach, the type of input and output data used, and the quality of the data. A critical discussion clarifies several interactions between the above and results in a logical flow that can be used as a framework for developing short‐term traffic forecasting models.

Suggested Citation

  • Eleni I. Vlahogianni & John C. Golias & Matthew G. Karlaftis, 2003. "Short‐term traffic forecasting: Overview of objectives and methods," Transport Reviews, Taylor & Francis Journals, vol. 24(5), pages 533-557, November.
    • Handle: RePEc:taf:transr:v:24:y:2003:i:5:p:533-557
    DOI: 10.1080/0144164042000195072
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    Cited by:

    1. Han Zheng & Junhua Chen & Zhaocha Huang & Kuan Yang & Jianhao Zhu, 2022. "Short-Term Online Forecasting for Passenger Origin–Destination (OD) Flows of Urban Rail Transit: A Graph–Temporal Fused Deep Learning Method," Mathematics, MDPI, vol. 10(19), pages 1-30, October.

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