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An Evolutionary Approach for Short-Term Traffic Flow Forecasting Service in Intelligent Transportation System

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Smart Computing and Communication (SmartCom 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10135))

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Abstract

In recent years, traffic flow prediction has become a crucial technique in ITS (intelligent transportation system), which is helpful for alleviating the congestion in many metropolises and improving the efficiency of public traffic service. On the other hand, with the development of traffic sensors, traffic data are collected with a fantastic scale. It leads ITS into a data-driven application fashion. With this observation, it is a challenge to accurately and promptly forecast the traffic flow by effectively utilizing the big traffic data. In view of this challenge, in this paper, we propose an evolutionary method for short-term traffic flow forecasting service. Concretely, in our method, traffic flow is firstly specified by a model of time series. Then, the model is decomposed into seasonal component and the residual component. The seasonal component reflects the history average condition, while we treat the residual component as the output of a linear filter. The proposed method is evaluated with real bus transaction dataset. The experimental results show the effectiveness of our method.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Fan Fei, Shu Li & Wanchun Dou

  2. The Department of Computer Science and Technology, Nanjing University, Nanjing, China

    Fan Fei, Shu Li & Wanchun Dou

  3. School of Information Technology, Deakin University, Melbourne, Australia

    Shui Yu

Authors
  1. Fan Fei
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  2. Shu Li
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  3. Wanchun Dou
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  4. Shui Yu
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Corresponding author

Correspondence to Wanchun Dou .

Editor information

Editors and Affiliations

  1. Pace University, New York, New York, USA

    Meikang Qiu

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Fei, F., Li, S., Dou, W., Yu, S. (2017). An Evolutionary Approach for Short-Term Traffic Flow Forecasting Service in Intelligent Transportation System. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2016. Lecture Notes in Computer Science(), vol 10135. Springer, Cham. https://doi.org/10.1007/978-3-319-52015-5_49

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  • DOI: https://doi.org/10.1007/978-3-319-52015-5_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52014-8

  • Online ISBN: 978-3-319-52015-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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