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Traffic state prediction using ISOMAP manifold learning

Author

Listed:
  • Liu, Qingchao
  • Cai, Yingfeng
  • Jiang, Haobin
  • Lu, Jian
  • Chen, Long

Abstract

Traffic state prediction is an essential problem with considerable implications in the intelligent transportation system. This paper puts forward an approach for predicting urban road traffic states based on ISOMAP manifold learning. By establishing a distance measurement that represents the overall geometric structure based on the Isometric Feature Mapping (ISOMAP) algorithm, this approach utilizes all consistent information regarding the traffic flow, thus improving the prediction accuracy of the road traffic state. The experimental results indicate that, compared with a traditional prediction approach, the equality coefficient has a bigger increase in value and a much lower prediction error. The traffic state prediction approach based on ISOMAP manifold learning achieves a higher level of accuracy.

Suggested Citation

  • Liu, Qingchao & Cai, Yingfeng & Jiang, Haobin & Lu, Jian & Chen, Long, 2018. "Traffic state prediction using ISOMAP manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 532-541.
    • Handle: RePEc:eee:phsmap:v:506:y:2018:i:c:p:532-541
    DOI: 10.1016/j.physa.2018.04.031
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    References listed on IDEAS

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    Cited by:

    1. Wu, Jiaxin & Zhou, Xubing & Peng, Yi & Zhao, Xiaojun, 2022. "Recurrence analysis of urban traffic congestion index on multi-scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    2. Xuguang Han & Jingming Su & Yan Hong & Pingshun Gong & Danping Zhu, 2022. "Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    3. Wenhao Li & Chengkun Liu & Tao Wang & Yanjie Ji, 2024. "An innovative supervised learning structure for trajectory reconstruction of sparse LPR data," Transportation, Springer, vol. 51(1), pages 73-97, February.
    4. Huang, Yan & Wan, Jiansong & Huang, Xin, 2019. "Quantitative analysis of financial system fragility based on manifold curvature," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1276-1285.

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