A Hybrid Deep Learning Method for Short-Term Traffic Flow Forecasting: GSA-LSTM

Bharti Naheliya; Poonam Redhu; Kranti Kumar

doi:10.17485/IJST/v16i46.2520

Article

A Hybrid Deep Learning Method for Short-Term Traffic Flow Forecasting: GSA-LSTM

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i46.2520

Year: 2023, Volume: 16, Issue: 46, Pages: 4358-4368

Original Article

A Hybrid Deep Learning Method for Short-Term Traffic Flow Forecasting: GSA-LSTM

Bharti Naheliya¹, Poonam Redhu^1*, Kranti Kumar²

¹Department of Mathematics, Maharshi Dayanand University, Rohtak, 124001, Haryana, India
²School of Liberal Studies, Dr. B. R. Ambedkar University, Delhi, 110006, New Delhi, India

*Corresponding Author
Email: [email protected]

Received Date:05 October 2023, Accepted Date:28 October 2023, Published Date:13 December 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The main objective of this study is to improve the accuracy and reliability of the short-term traffic flow forecasting method, while simultaneously addressing limitations in existing models and proposing a novel approach that enhances the quality of the traffic flow predictions. Methods: This study developed a long short-term memory (LSTM) neural network optimized by the gravitational search approach (GSA) to enhance prediction accuracy for short-term traffic flow. The gravitational search algorithm selects the best parameters for the long short-term memory neural network on a global scale. Findings: The proposed GSA-LSTM model exhibits significant superiority over the selected models when assessing performance through the evaluation metrics such as root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (r). Moreover, the average accuracy of the proposed model is higher as compared to other existing neural network models which depicted the effectiveness of the proposed model. Novelty: Tables and figures displayed that the performance accuracy of the proposed model is higher than the other selected models such as autoregressive integrated moving average (ARIMA), wavelet neural network (WNN), Gated Recurrent Unit (GRU), long short-term memory model (LSTM), GSA-GRU, ACO-LSTM, and PSO-LSTM model.

Keywords: Intelligent Transportation System, Deep Learning, Traffic Flow Prediction

References

Mondal MA, Rehena Z. Intelligent traffic congestion classification system using artificial neural network. In: WWW '19: Companion Proceedings of The 2019 World Wide Web Conference. p. 110–116.
Yang H, Li X, Qiang W, Zhao Y, Zhang W, Tang C. A network traffic forecasting method based on SA optimized ARIMA–BP neural network. Computer Networks. 2021;193:108102. Available from: https://doi.org/10.1016/j.comnet.2021.108102
Dai G, Tang J, Luo W. Short-term traffic flow prediction: An ensemble machine learning approach. Alexandria Engineering Journal. 2023;74:467–480. Available from: https://doi.org/10.1016/j.aej.2023.05.015
Cai L, Chen Q, Cai W, Xu X, Zhou T, Qin J. SVRGSA: a hybrid learning based model for short‐term traffic flow forecasting. IET Intelligent Transport Systems. 2019;13(9):1348–1355. Available from: https://doi.org/10.1049/iet-its.2018.5315
Zhou S, Wei C, Song C, Fu Y, Luo R, Chang W, et al. A Hybrid Deep Learning Model for Short-Term Traffic Flow Pre-Diction Considering Spatiotemporal Features. Sustainability. 2022;14(16):1–14. Available from: https://doi.org/10.3390/su141610039
Bharti, Redhu P, Kumar K. Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM. Physica A: Statistical Mechanics and its Applications. 2023;625:129001. Available from: https://doi.org/10.1016/j.physa.2023.129001
Kashyap AA, Raviraj S, Devarakonda A, K SRN, V SK, Bhat SJ. Traffic flow prediction models – A review of deep learning techniques. Cogent Engineering. 2022;9(1):1–24. Available from: https://doi.org/10.1080/23311916.2021.2010510
Zhang X, Wang C, Chen J, Chen D. A deep neural network model with GCN and 3D convolutional network for short‐term metro passenger flow forecasting. IET Intelligent Transport Systems. 2023;17(8):1599–1607. Available from: https://doi.org/10.1049/itr2.12352
Shuai C, Pan Z, Gao L, Zuo H. Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition. Advances in Civil Engineering. 2021;2021:1–10. Available from: https://doi.org/10.1155/2021/4313970
Agafonov AA. Short-Term Traffic Data Forecasting: A Deep Learning Approach. Optical Memory and Neural Networks. 2021;30(1):1–10. Available from: https://doi.org/10.3103/S1060992X21010021
Zhezheng F. Short-Term Traffic Flow Prediction Method with Multiple Factors and Deep Learning. In: 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI). Changchun, China, 26-28 May 2023. IEEE. .
Lu H, Huang D, Song Y, Jiang D, Zhou T, Qin J. ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting. Electronics. 2020;9(9):1–17. Available from: https://doi.org/10.3390/electronics9091474
Zhou T, Dou H, Tan J, Song Y, Wang F, Wang J. Small dataset solves big problem: An outlier-insensitive binary classifier for inhibitory potency prediction. Knowledge-Based Systems. 2022;251:109242. Available from: https://doi.org/10.1016/j.knosys.2022.109242
Tang J, Zeng J, Wang Y, Yuan H, Liu F, Huang H. Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm. Transportmetrica A: Transport Science. 2021;17(4):1217–1243. Available from: https://doi.org/10.1080/23249935.2020.1845250
Olayode IO, Tartibu LK, Okwu MO, Severino A. Comparative Traffic Flow Prediction of a Heuristic ANN Model and a Hybrid ANN-PSO Model in the Traffic Flow Modelling of Vehicles at a Four-Way Signalized Road Intersection. Sustainability. 2019;13(19):1–28. Available from: https://doi.org/10.3390/su131910704
Chen Q, Song Y, Zhao J. Short-term traffic flow prediction based on improved wavelet neural network. Neural Computing and Applications. 2021;33(14):8181–8190. Available from: https://doi.org/10.1007/s00521-020-04932-5
Cai W, Yang J, Yu Y, Song Y, Zhou T, Qin J. PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting. IEEE Access. 2020;8:6505–6514. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8949498
Cui Z, Huang B, Dou H, Tan G, Zheng S, Zhou T. GSA‐ELM: A hybrid learning model for short‐term traffic flow forecasting. IET Intelligent Transport Systems. 2022;16(1):41–52. Available from: https://doi.org/10.1049/itr2.12127
Xie Q, Song L, He Y, Dang P. Short-term Wind Power Prediction Model Based on GSA Optimized GRU Neural Network. In: 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA). Chongqing, China, 06-08 November 2020. IEEE. .
Wei Y, Zheng S, Yang X, Huang B, Tan G, Zhou T. A noise-immune extreme learning machine for short-term traffic flow forecasting. In: International Conference on Smart Transportation and City Engineering 2021. Chongqing, China. SPIE. 12050.
Mao Y, Qin G, Ni P, Liu Q. Analysis of road traffic speed in Kunming plateau mountains: a fusion PSO-LSTM algorithm. International Journal of Urban Sciences. 2022;26(1):87–107. Available from: https://doi.org/10.1080/12265934.2021.1882331
Rashedi E, Nezamabadi-pour H, Saryazdi S. GSA: A Gravitational Search Algorithm. Information Sciences. 2009;179(13):2232–2248. Available from: https://doi.org/10.1016/j.ins.2009.03.004
Pelusi D, Mascella R, Tallini L, Nayak J, Naik B, Deng Y. Improving exploration and exploitation via a Hyperbolic Gravitational Search Algorithm. Knowledge-Based Systems. 2020;193:105404. Available from: https://doi.org/10.1016/j.knosys.2019.105404
Yongsheng D, Fengshun J, Jie Z, Zhikeng L. A Short-Term Power Output Forecasting Model Based on Correlation Analysis and ELM-LSTM for Distributed PV System. Journal of Electrical and Computer Engineering. 2020;2020:1–10. Available from: https://doi.org/10.1155/2020/2051232

Copyright

© 2023 Naheliya et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)