Traffic Flow Prediction Model Based on BDBO-TCN

Zhang Xijun, Xuan Chen

2024

Abstract

In order to improve the accuracy of short-term traffic flow prediction and overcome the shortcomings of single prediction model and the limitations of traditional depth learning based on experience to set hyperparameters, a time convolution network (TCN) model based on improved dung beetle algorithm (DBO) is proposed. In order to solve the problem of slow convergence of traditional TCN model, the dung beetle algorithm is introduced, and the Bernoulli chaotic mapping algorithm is used to improve the initial value, considering the randomness and diversity of the initialization of dung beetle algorithm, the traffic flow prediction model based on BDBO-TCN is constructed. To verify the predictive effect of the experiment, experiments were conducted on two different real data sets, the multi-step prediction is compared with the TCN model optimized by DBO based on various chaotic mapping algorithms to further verify the prediction performance of the model. This model is superior to other models.

Paper Citation

in Harvard Style

Xijun Z. and Chen X. (2024). Traffic Flow Prediction Model Based on BDBO-TCN. In Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS; ISBN 978-989-758-715-3, SciTePress, pages 31-36. DOI: 10.5220/0012876100004536

in Bibtex Style

@conference{dmeis24,
author={Zhang Xijun and Xuan Chen},
title={Traffic Flow Prediction Model Based on BDBO-TCN},
booktitle={Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS},
year={2024},
pages={31-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012876100004536},
isbn={978-989-758-715-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS
TI - Traffic Flow Prediction Model Based on BDBO-TCN
SN - 978-989-758-715-3
AU - Xijun Z.
AU - Chen X.
PY - 2024
SP - 31
EP - 36
DO - 10.5220/0012876100004536
PB - SciTePress