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Forecasting Traffic Flow: Short Term, Long Term, and When It Rains

  • Hao PengEmail author
  • Santosh U. Bobade
  • Michael E. Cotterell
  • John A. Miller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10968)

Abstract

Forecasting is the art of taking available information of the past and attempting to make the best educated guesses of the ever unforeseen future. From the historical data, patterns can be observed and forecasting models have been developed to capture such patterns. This work focuses on forecasting traffic flow in major urban areas and freeways in the state of Georgia using large amounts of data collected from traffic sensors. Much of the existing work on traffic flow forecasting focuses on the immediate short terms. In addition to that, this work studies the forecasting powers of various models, including seasonal ARIMA, exponential smoothing and neural networks, for relatively long terms. A second experiment that incorporates precipitation data into forecasting models to better predict traffic flow in rainy weather is also conducted. Dynamic regression models and neural networks are used in this experiment. In both experiments, neural networks outperformed the others overall.

Keywords

Traffic flow forecasting Big data analytics Time series analysis Seasonal ARIMA Exponential smoothing Neural networks Dynamic regression 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of GeorgiaAthensUSA

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