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Fractal Analysis of the Relation between the Observation Scale and the Prediction Cycle in Short-Term Traffic Flow Prediction

  • Sheng ZhangEmail author
  • Zhong-xiang Huang
Article
  • 102 Downloads

Abstract

Based on the analysis of the field traffic flow time series, we found that there is self-similarity and periodic similarity in the traffic flow of different observation scales, which makes the short-term traffic flow prediction a meaningful work. For the purpose of finding the smallest prediction cycle, fractal analysis was conducted in the relation between the observation scale and the prediction cycle by using both the field data and the simulated data. We calculate the fractal dimension and the scaling region of traffic flow time series by using the G-P algorithm. If the scaling region can be found in the traffic flow time series at some observation scale, it means that there is self-similarity in the time series at that observation scale. The minimum observation scale at which there is self-similarity in the traffic flow is the smallest prediction cycle. This observation scale is a prerequisite for judging whether the traffic flow can be predicted or not. This research provides a reference for the short-term traffic flow prediction on expressway and urban road.

Keywords

Short-term traffic flow prediction Observation scale Fractal theory G-P algorithm 

Notes

Acknowledgments

We are indebted to the Associate Editor and our three anonymous referees for their thoughtful comments that have helped substantially improve this work. This research has been substantially supported by the research grants from the National Natural Science Foundation Council of China (51408058, 51338002, and 51508041), and Open Fund of Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety(Changsha University of Science & Technology), Ministry of Education, under grant number kfj130301.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Traffic and Transportation EngineeringChangsha University of Science & TechnologyChangshaPeople’s Republic of China

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