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Data, Methods, and Applications of Traffic Source Prediction

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Transportation Analytics in the Era of Big Data

Part of the book series: Complex Networks and Dynamic Systems ((CNDS,volume 4))

Abstract

Traffic source prediction provides a new way of mitigating traffic congestion. Since the initial discovery that the major usage of road segments can be traced to surprisingly few driver sources, studies of traffic source prediction have recently experienced rapid development. With more high-resolution traffic data available, dynamical driver sources and passenger sources have been proposed, and the method of targeting traffic sources highly related to congestion has triggered a number of applications ranging from travel demand control to vehicle routing guidance and infrastructure upgrades. Here, we present a comprehensive review of the data, methods, and applications of traffic source prediction.

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Author information

Authors and Affiliations

  1. School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, China

    Chengcheng Wang & Pu Wang

Authors
  1. Chengcheng Wang
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  2. Pu Wang
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Corresponding author

Correspondence to Pu Wang .

Editor information

Editors and Affiliations

  1. School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA

    Satish V. Ukkusuri

  2. Tongji University, Shanghai, China

    Chao Yang

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Wang, C., Wang, P. (2019). Data, Methods, and Applications of Traffic Source Prediction. In: Ukkusuri, S., Yang, C. (eds) Transportation Analytics in the Era of Big Data. Complex Networks and Dynamic Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-75862-6_5

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