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Spatiotemporal traffic forecasting: review and proposed directions

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  • Alireza Ermagun
  • David Levinson

Abstract

This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. We extract and synthesise 130 research papers, considering two perspectives: (1) methodological framework and (2) methods for capturing spatial information. Spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. Machine learning methods, which have attracted more attention in recent years, outperform the naïve statistical methods such as historical average and exponential smoothing. However, there is no guarantee of superiority when machine learning methods are compared with advanced statistical methods such as spatiotemporal autoregressive integrated moving average. As for the spatial dependency detection, a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks as follows: (1) studies capture spatial dependency of either adjacent or distant upstream and downstream links with the study link, (2) the spatially relevant links are selected either by prejudgment or by correlation-coefficient analysis, and (3) studies develop forecasting methods in a corridor test sample, where all links are connected sequentially together, assume a similarity between the behaviour of both parallel and adjacent links, and overlook the competitive nature of traffic links.

Suggested Citation

  • Alireza Ermagun & David Levinson, 2018. "Spatiotemporal traffic forecasting: review and proposed directions," Transport Reviews, Taylor & Francis Journals, vol. 38(6), pages 786-814, November.
  • Handle: RePEc:taf:transr:v:38:y:2018:i:6:p:786-814
    DOI: 10.1080/01441647.2018.1442887
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    Cited by:

    1. Huan Ngo & Sabyasachee Mishra, 2023. "Traffic Graph Convolutional Network for Dynamic Urban Travel Speed Estimation," Networks and Spatial Economics, Springer, vol. 23(1), pages 179-222, March.
    2. Chikaraishi, Makoto & Garg, Prateek & Varghese, Varun & Yoshizoe, Kazuki & Urata, Junji & Shiomi, Yasuhiro & Watanabe, Ryuki, 2020. "On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis," Transport Policy, Elsevier, vol. 98(C), pages 91-104.
    3. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    4. Sergei V. Shalagin, 2021. "Computing a Group of Polynomials over a Galois Field in FPGA Architecture," Mathematics, MDPI, vol. 9(24), pages 1-10, December.
    5. Mohandu Anjaneyulu & Mohan Kubendiran, 2022. "Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    6. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    7. Gutierrez-Lythgoe, Antonio, 2023. "Movilidad urbana sostenible: Predicción de demanda con Inteligencia Artificial [Sustainable Urban Mobility: Demand Prediction with Artificial Intelligence]," MPRA Paper 117103, University Library of Munich, Germany.
    8. Hu, Xu & Li, Dongshuang & Yu, Zhaoyuan & Yan, Zhenjun & Luo, Wen & Yuan, Linwang, 2022. "Quantum harmonic oscillator model for fine-grained expressway traffic volume simulation considering individual heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).

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