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Advancing Short-Term Traffic Congestion Prediction: Navigating Challenges in Learning-Based Approaches

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Applied Intelligence (ICAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2015))

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Abstract

Traffic congestion prediction has already become a significant aspect of modern transportation systems. By predicting traffic congestion, transportation planners and traffic management agencies can take steps to reduce congestion and improve traffic flow, and also inform the public about expected delays and suggest alternative routes, helping people to make more informed decisions about their travel plans. A growing body of literature has provided different methods in order to improve congestion management abilities to intelligent traffic systems, it is a trending research topic facilitating the development of transportation system prediction. This paper reviews and analyzes broadband of published articles regarding different approaches focusing on short-term real-time traffic predictions; then stresses on five core methodologies for a detailed analysis and provides a discussion on the pros and cons among different approaches with test results. In addition, this paper develops a perspective synthesis of the current status quo that could be the next steps for a more accurate, more efficient prediction. In the end, the paper yields conclusions about possible future research endeavors.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Alabama, BOX 870290, Tuscaloosa, AL, 35487-0290, USA

    Chen Wang & Travis Atkison

  2. Department of Civil, Construction and Environmental Engineering, The University of Alabama, BOX 870205, Tuscaloosa, AL, 35487-0205, USA

    Qiuhua Duan

Authors
  1. Chen Wang
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  2. Travis Atkison
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  3. Qiuhua Duan
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Corresponding author

Correspondence to Chen Wang .

Editor information

Editors and Affiliations

  1. Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Guangxi Academy of Sciences, Guangxi, China

    Changan Yuan

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wang, C., Atkison, T., Duan, Q. (2024). Advancing Short-Term Traffic Congestion Prediction: Navigating Challenges in Learning-Based Approaches. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2015. Springer, Singapore. https://doi.org/10.1007/978-981-97-0827-7_1

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  • DOI: https://doi.org/10.1007/978-981-97-0827-7_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0826-0

  • Online ISBN: 978-981-97-0827-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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