NxLFTNet_BGSO: NARX LSTM Forward Taylor Network Enabled Al-Biruni Group Search Optimization for Traffic Forecasting
Keywords:
Traffic forecasting, Spatio-Temporal Embedding (STE) generator, Long Short Term Memory (LSTM), Group Search Optimizer (GSO), Al-Biruni Earth Radius (BER).Abstract
Traffic signals utilized to forecast are commonly produced by sensors next to the roads that may be indicated as nodes on a graph. Normally, these sensors generate common signals indicating traffic flows and abnormal signals represent unknown traffic disruptions. Graph convolution networks are broadly deployed for traffic forecasting owing to the capability for capturing the relation amid network nodes. Nevertheless, the task is difficult owing to the expected intricacy and improbability of traffic patterns. To address this shortcoming, a traffic forecasting model is established with Nonlinear Autoregressive models with exogenous inputs Long Short Term Memory Forward Taylor Network (NxLFTNet) enabled Al-Biruni Group search optimization (BGSO_NxLFTNet). The input traffic network is given to spatio-temporal embedding (STE) generator for identifying daily and weekly embedding of time corresponding to current traffic signal. The outcome of input traffic network and STE generator is subjected to traffic detection, which is executed by employing NxLFTNet trained by BGSO. Here, NxLFTNet is combined by NARX and LSTM; also BGSO is incorporated by Group Search Optimizer (GSO) and Al-Biruni Earth Radius (BER). The measures taken for BGSO_NxLFTNet such as, Mean absolute percentage error (MAPE), Root Mean square error (RMSE), and Mean Absolute Error (MAE) gained 0.001, 0.010 and 0.011.
Downloads
References
B. N. Kamath R. Fernandes, A. P. Rodrigues, M. Mahmud, P. Vijaya, T. R. Gadekallu, and M. S. Kaiser, “TAKEN: a traffic knowledge-based navigation system for connected and autonomous vehicles”, Sensors, vol. 23, no. 2, pp.653, 2023.
B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting”, arXiv preprint arXiv: 1709.04875, 2017.
M. E. Ben-Akiva, S. Gao, Z. Wei, and Y. Wen, “A dynamic traffic assignment model for highly congested urban networks”, Transportation research part C: emerging technologies, vol. 24, pp.62-82, 2012.
A. Hamilton, B. Waterson, T. Cherrett, A. Robinson, and I. Snell, “The evolution of urban traffic control: changing policy and technology”, Transportation planning and technology, vol. 36, no. 1, pp.24-43, 2013.
H. F. Yang, T. S. Dillon, and Y. P. P. Chen, “Optimized structure of the traffic flow forecasting model with a deep learning approach”, IEEE transactions on neural networks and learning systems, vol. 28, no. 10, pp.2371-2381, 2016.
K. Boriboonsomsin, M. J. Barth, W. Zhu, and A. Vu, “Eco-routing navigation system based on multisource historical and real-time traffic information”, IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp.1694-1704, 2012.
M. Veres, and M. Moussa, “Deep learning for intelligent transportation systems: A survey of emerging trends”, IEEE Transactions on Intelligent transportation systems, vol. 21, no. 8, pp.3152-3168, 2019.
H. Lu, D. Huang, Y. Song, D. Jiang, T. Zhou, and J. Qin, “St-trafficnet: A spatial-temporal deep learning network for traffic forecasting”, Electronics, vol. 9, no. 9, pp.1474, 2020.
R. Yu, Y. Li, C. Shahabi, U. Demiryurek, and Y. Liu, “Deep learning: A generic approach for extreme condition traffic forecasting”, In Proceedings of the 2017 SIAM international Conference on Data Mining, Society for Industrial and Applied Mathematics, pp. 777-785, June 2017.
J. Nahar, Y. P. P. Chen, and S. Ali, “Kernel-based naive bayes classifier for breast cancer prediction”, Journal of biological systems, vol. 15, no. 01, pp.17-25. 2007.
K. Y. Chan, S. Khadem, T. S. Dillon, V. Palade, J. Singh, and E. Chang, “Selection of significant on-road sensor data for short-term traffic flow forecasting using the Taguchi method”, IEEE Transactions on Industrial Informatics, vol. 8, no. 2, pp.255-266, 2011.
K. Y. Chan, T. Dillon, E. Chang, and J. Singh, “Prediction of short-term traffic variables using intelligent swarm-based neural networks”, IEEE Transactions on Control Systems Technology, vol. 21, no. 1, pp.263-274, 2012.
S. Kundu, M. S. Desarkar, and P. K. Srijith, “Traffic forecasting with deep learning”, In proceedings of 2020 IEEE Region 10 Symposium (TENSYMP), pp. 1074-1077, June 2020.
Y. Zhang, T. Zhao, S. Gao, and M. Raubal, “Incorporating multimodal context information into traffic speed forecasting through graph deep learning”, International Journal of Geographical Information Science, vol. 37, no. 9, pp. 1909-1935, 2023.
Weng, Wenchao, J. Fan, H. Wu, Y. Hu, H. Tian, F. Zhu, and Jia Wu., "A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting”, Pattern Recognition, vol. 142, 2023.
Y. Gao, C. Zhou, J. Rong, Y. Wang, and S. Liu, “Short-term traffic speed forecasting using a deep learning method based on multitemporal traffic flow volume”, IEEE Access, vol. 10, pp. 82384-82395, 2022.
W. Fang, W. Zhuo, J. Yan, Y. Song, D. Jiang, and T. Zhou, “Attention meets long short-term memory: A deep learning network for traffic flow forecasting”, Physica A: Statistical Mechanics and its Applications, vol. 587, pp. 126485, 2022.
S. T. Nabi, M. R. Islam, M. G. R. Alam, M. M. Hassan, S. A. AlQahtani, G. Aloi, and G. Fortino, “Deep learning based fusion model for multivariate LTE traffic forecasting and optimized radio parameter estimation”, IEEE Access, vol. 11, pp.14533-14549, 2023.
C. Ma, K. Sun, L. Chang, and Z. Qu, “Enhanced Information Graph Recursive Network for Traffic Forecasting” Electronics, vol. 12, no. 11, pp.2519, 2023.
Y. Ma, Z. Zhang, and A. Ihler, “Multi-lane short-term traffic forecasting with convolutional LSTM network”, IEEE Access, vol. 8, pp.34629-34643, 2020.
Y. Shin, and Y. Yoon, “PGCN: Progressive graph convolutional networks for spatial–temporal traffic forecasting”, IEEE Transactions on Intelligent Transportation Systems, 2024.
Large ST dataset will be taken from “https://github.com/liuxu77/LargeST?tab=readme-ov-file”, accessed on May 2024.
Q. Liu, W. Chen, H. Hu, Q. Zhu, and Z. Xie, “An optimal NARX neural network identification model for a magnetorheological damper with force-distortion behavior”, Frontiers in Materials, vol.7, pp.10, 2020.
A. Sagheer, and M. Kotb, "Time series forecasting of petroleum production using deep LSTM recurrent networks", Neurocomputing, vol. 323, pp.203-213, 2019.
L. Abualigah, “Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications”, Neural Computing and Applications, vol. 33, no. 7, pp.2949-2972, 2021.
E. S. M. El-Kenawy, A. A. Abdelhamid, A. Ibrahim, S. Mirjalili, N. Khodadad, M. A. Alduailij, A. A. Alhussan, and D. S. Khafaga, “Al-Biruni Earth Radius (BER) Metaheuristic Search Optimization Algorithm”, Comput. Syst. Sci. Eng., vol. 45, no. 2, pp.1917-1934, 2023.
S. H. Bhojani, and N. Bhatt, “Wheat crop yield prediction using new activation functions in neural network”, Neural Computing and Applications, vol. 32, no. 17, pp.13941-13951, 2020.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
