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

Yuqi Lin

doi:10.62762/TIS.2024.952592

CiteScore

1.08

Impact Factor

Volume 1, Issue 2, IECE Transactions on Intelligent Systematics

Volume 1, Issue 2, 2024

Submit Manuscript Edit a Special Issue

Academic Editor

Quanmin Zhu

University of the West of England, United Kingdom

Article QR Code

Scan the QR code for reading

Popular articles

Research on A Ship Trajectory Classification Method Based on Deep Learning YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data Visual Feature Extraction and Tracking Method Based on Corner Flow Detection A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval Inaugural Editorial of the Chinese Journal of Information Fusion Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform

IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 2: 79-90

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: [email protected]

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

References

[1] Ma, C., Zhao, Y., Dai, G., Xu, X., & Wong, S. C. (2022). A novel STFSA-CNN-GRU hybrid model for short-term traffic speed prediction. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3728-3737.

[2] Zhang, J., Cui, Y., & Ren, J. (2022). Dynamic mission planning algorithm for UAV formation in battlefield environment. IEEE Transactions on Aerospace and Electronic Systems, 59(4), 3750-3765.

[3] Ma, C., Dai, G., & Zhou, J. (2021). Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM-BILSTM method. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5615-5624.

[4] Kong, J., Yang, C., Wang, J., Wang, X., Zuo, M., Jin, X., & Lin, S. (2021). Deep-stacking network approach by multisource data mining for hazardous risk identification in IoT-based intelligent food management systems. Computational Intelligence and Neuroscience, 2021(1), 1194565.

[5] Jiang, R., Han, S., Yu, Y., & Ding, W. (2023). An access control model for medical big data based on clustering and risk. Information Sciences, 621, 691-707.

[6] Tian, Y., & Pan, L. (2015, December). Predicting short-term traffic flow by long short-term memory recurrent neural network. In 2015 IEEE international conference on smart city/SocialCom/SustainCom (SmartCity) (pp. 153-158). IEEE.

[7] Jin, X. B., Wang, Z. Y., Kong, J. L., Bai, Y. T., Su, T. L., Ma, H. J., & Chakrabarti, P. (2023). Deep spatio-temporal graph network with self-optimization for air quality prediction. Entropy, 25(2), 247.

[8] Jin, X., Zhang, J., Kong, J., Su, T., & Bai, Y. (2022). A reversible automatic selection normalization (RASN) deep network for predicting in the smart agriculture system. Agronomy, 12(3), 591.

[9] Lydia, M., Kumar, S. S., Selvakumar, A. I., & Kumar, G. E. P. (2016). Linear and non-linear autoregressive models for short-term wind speed forecasting. Energy conversion and management, 112, 115-124.

[10] Yang, B., Sun, S., Li, J., Lin, X., & Tian, Y. (2019). Traffic flow prediction using LSTM with feature enhancement. Neurocomputing, 332, 320-327.

[11] Meng, X., Fu, H., Peng, L., Liu, G., Yu, Y., Wang, Z., & Chen, E. (2020). D-LSTM: Short-term road traffic speed prediction model based on GPS positioning data. IEEE Transactions on Intelligent Transportation Systems, 23(3), 2021-2030.

[12] Chen, C., Liu, Z., Wan, S., Luan, J., & Pei, Q. (2020). Traffic flow prediction based on deep learning in internet of vehicles. IEEE transactions on intelligent transportation systems, 22(6), 3776-3789.

[13] Connor, J. T., Martin, R. D., & Atlas, L. E. (1994). Recurrent neural networks and robust time series prediction. IEEE transactions on neural networks, 5(2), 240-254.

[14] Jaitly, N., Le, Q. V., Vinyals, O., Sutskever, I., Sussillo, D., & Bengio, S. (2016). An online sequence-to-sequence model using partial conditioning. Advances in neural information processing systems, 29.

[15] Chen, Z., Chen, L., Shen, W., & Xu, K. (2021). Remaining useful life prediction of lithium-ion battery via a sequence decomposition and deep learning integrated approach. IEEE Transactions on Vehicular Technology, 71(2), 1466-1479.

[16] Niu, Z., Zhong, G., & Yu, H. (2021). A review on the attention mechanism of deep learning. Neurocomputing, 452, 48-62.

[17] Jin, X. B., Wang, Z. Y., Gong, W. T., Kong, J. L., Bai, Y. T., Su, T. L., ... & Chakrabarti, P. (2023). Variational bayesian network with information interpretability filtering for air quality forecasting. Mathematics, 11(4), 837.

[18] Qiu, Z., Zhu, T., Jin, Y., Sun, L., & Du, B. (2023). A graph attention fusion network for event-driven traffic speed prediction. Information Sciences, 622, 405-423.

[19] Kao, I. F., Zhou, Y., Chang, L. C., & Chang, F. J. (2020). Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting. Journal of Hydrology, 583, 124631.

[20] Du, S., Li, T., Yang, Y., & Horng, S. J. (2020). Multivariate time series forecasting via attention-based encoder–decoder framework. Neurocomputing, 388, 269-279.

[21] Kong, J. L., Fan, X. M., Jin, X. B., Su, T. L., Bai, Y. T., Ma, H. J., & Zuo, M. (2023). BMAE-Net: A data-driven weather prediction network for smart agriculture. Agronomy, 13(3), 625.

[22] Shi, B., Ou, Y., Wang, D., & Zhao, G. (2024). Self-Organizing Hierarchical Incremental Learning Framework and Universal Approximation Analysis Based on Stochastic Configuration Mechanism. Information Sciences, 121402.

[23] Wang, D., & Li, M. (2017). Stochastic configuration networks: Fundamentals and algorithms. IEEE transactions on cybernetics, 47(10), 3466-3479.

[24] Chen, X., Chen, Y., and He, Z.(2021). Urban Traffic Speed Dataset of Guangzhou, China. Zenodo.

[25] Wang, Y., Liao, W., & Chang, Y. (2018). Gated recurrent unit network-based short-term photovoltaic forecasting. Energies, 11(8), 2163.

Cite This Article

APA Style

Lin, Y. (2024). Long-term Traffic Flow Prediction using Stochastic Configuration Networks for Smart Cities. IECE Transactions on Intelligent Systematics, 1(2), 79-90. https://doi.org/10.62762/TIS.2024.952592

Article Metrics

Citations:

Google Scholar

Crossref

Scopus

Web of Science

Article Access Statistics:

PDF Downloads: 32

Publisher's Note

IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

IECE Transactions on Intelligent Systematics

ISSN: 2998-3355 (Online) | ISSN: 2998-3320 (Print)

Email: [email protected]

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Google Scholar

Crossref

Scopus

Web of Science

We use cookies