Long-term Traffic Flow Prediction using Stochastic Configuration Networks for Smart Cities

Yuqi Lin

doi:10.62762/TIS.2024.952592

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Volume 1, Issue 2, IECE Transactions on Intelligent Systematics

Volume 1, Issue 2, 2024

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Quanmin Zhu

University of the West of England, United Kingdom

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IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 2: 68-79

Free Access | Research Article | 27 September 2024

Long-term Traffic Flow Prediction using Stochastic Configuration Networks for Smart Cities

Yuqi Lin 1 *

1 The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana 61801, United States

* Corresponding author: Yuqi Lin, email: yuqilin2@illinois.edu

DOI: 10.62762/TIS.2024.952592

Received: 26 August 2024, Accepted: 22 September 2024, Published: 27 September 2024

Abstract

Accurate predictions of traffic flow are very meaningful to city managers. With such information, traffic systems can better coordinate traffic signals and reduce congestion. By understanding traffic patterns, navigation systems can provide real-time routing suggestions that avoid traffic jams, save time, and reduce fuel consumption. However, traffic flow will be interfered with by multiple factors such as collection time and place. In this paper, we propose to use stochastic configuration networks (SCNs) to predict the traffic flow. The network is trained through stepwise construction, and the network parameters are effectively optimized based on the approximation theorem and convergence analysis optimization mechanism. The proposed network automatically adjusts its structure according to the complexity of traffic flow to better adapt to the complex non-linearity of traffic flow. We observed that the proposed model achieves better prediction performance overall and greater flexibility in the length of the prediction period compared to the benchmarks using the Guangzhou urban traffic flow dataset. It's worth noting that SCNs consistently outperform other models across different prediction intervals. They yield RMSE improvements of up to 10.73% for 10-minute predictions, 5.02% for 30-minute predictions, and 11.21% for 60-minute predictions compared to the least effective models. The R-value also exhibits steady enhancement, increasing up to 0.78%, 0.65%, and 2.33% for 10-minute, 30-minute, and 60-minute predictions, respectively. These notable advancements, combined with the model's computational efficiency, especially in short-term predictions, underscore the effectiveness and practicality of SCNs in traffic flow prediction tasks.

Graphical Abstract

Keywords

Traffic flow prediction

time-series data prediction

stochastic configuration network(SCNs)

deep learning

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APA Style

Lin, Y. (2024). Long-term Traffic Flow Prediction using Stochastic Configuration Networks for Smart Cities. IECE Transactions on Intelligent Systematics, 1(2), 68–79. https://doi.org/10.62762/TIS.2024.952592

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