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TRELM-DROP: An impavement non-iterative algorithm for traffic flow forecast

Author

Listed:
  • Yang, Yuwei
  • Li, Zhuoxuan
  • Chen, Jun
  • Liu, Zhiyuan
  • Cao, Jinde

Abstract

Accurate prediction of traffic flow is crucial to building a smart city. Given the nonlinearity of traffic flow, this paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence combined with the DROP strategy. The algorithm is referred to as TRELM-DROP. The Tent chaos strategy and residual correction method reduce the impact of randomness in traffic flow. On this basis, the Tent and residual correction strategy avoids the weight optimization of the ELM algorithm using the iterative method. A DROP strategy is proposed in the proposed algorithm to improve its ability to predict traffic flow under varying conditions. A comprehensive comparison of 36 real-world datasets is presented in this paper, comparing TRELM-DROP with other benchmark models. The results show that the proposed algorithm can produce the best prediction performance regarding various prediction error metrics under various traffic conditions without iterative optimization.

Suggested Citation

  • Yang, Yuwei & Li, Zhuoxuan & Chen, Jun & Liu, Zhiyuan & Cao, Jinde, 2024. "TRELM-DROP: An impavement non-iterative algorithm for traffic flow forecast," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    • Handle: RePEc:eee:phsmap:v:633:y:2024:i:c:s0378437123008920
    DOI: 10.1016/j.physa.2023.129337
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    References listed on IDEAS

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    1. Liu, Qingchao & Liu, Tao & Cai, Yingfeng & Xiong, Xiaoxia & Jiang, Haobin & Wang, Hai & Hu, Ziniu, 2021. "Explanatory prediction of traffic congestion propagation mode: A self-attention based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    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.
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