ArticleScientific reports2025
Traffic flow prediction based on improved deep extreme learning machine.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Bilevel multiobjective control enhances arterial performance via spatiotemporal optimization of presignalized intersections.Scientific reports · 2026Article
- A spatio-temporal self-supervised meta-learning network with dynamic graph learning for traffic flow forecasting.PloS one · 2026Article
- Short-term traffic flow prediction research based on ICEEMDAN-MPE-PSO-DELM model.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
A new hybrid prediction model is proposed for short-term traffic flow, which is based on Deep Extreme Learning Machine improved by Sparrow Search Algorithm (SSA-DELM). Firstly, Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm (ICEEMDAN) is employed to improve prediction accuracy. Then multiple Intrinsic Mode Function components (IMF) can be obtained. Secondly, Permutation Entropy algorithm (PE) is used to analyze the randomness of IMFs. Finally, different prediction models can be built according to the randomness characteristics. SSA-DELM prediction models are established for IMFs with large permutation entropy values. The IMFs with small permutation entropy values are put into ARIMA prediction models. To obtain the predicted traffic flow, different IMFs predicted values are added together. Two actual signalized intersections are selected to verify the performance of the new proposed model in this paper. Several prediction models based on different algorithms are built. The results obtained by MATLAB software show that the prediction errors of the new proposed model are the smallest and the fitting effect with the measured data is the best, which can effectively improve prediction accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.