Hybrid Neuro Genetic Causal Convolution Based Autoencoders for Traffic Prediction in Smart Cities

被引:0
|
作者
Thangarasu, Gunasekar [1 ]
Alla, Kesava Rao [2 ]
Kannan, K. Nattar [3 ]
机构
[1] IMU Univ, Saveetha Inst Med & Tech Sci, Saveetha Sch Engn, Dept Digital Hlth & Hlth Informat, Kuala Lumpur, Malaysia
[2] MAHSA Univ, Saujana Putra, Selangor, Malaysia
[3] Saveetha Inst Med & Tech Sci, Saveetha Sch Engn, Chennai, Tamil Nadu, India
来源
2024 IEEE 14TH SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS, ISCAIE 2024 | 2024年
关键词
Hybrid Neuro-Genetic; Casual Convolution; Autoencoders; Traffic Prediction;
D O I
10.1109/ISCAIE61308.2024.10576463
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This research presents a novel approach to traffic prediction in smart cities using Hybrid Neuro-Genetic Causal Convolution based Autoencoders (HNG-CCA). Urban traffic congestion has become a significant challenge in modern urban planning, necessitating accurate and efficient predictive models. In this work, we propose a hybrid architecture that combines neuro-genetic techniques with causal convolutional autoencoders to enhance the predictive capabilities of traffic patterns. The neuro-genetic approach optimizes the autoencoder architecture, while the causal convolutional layers capture the temporal dependencies inherent in traffic data. Our experiments on real-world traffic datasets demonstrate that the HNG-CCA outperforms existing methods in terms of prediction accuracy and generalization. This hybrid approach not only contributes to the field of traffic prediction but also showcases the potential of combining diverse machine learning paradigms to address complex urban challenges in smart cities.
引用
收藏
页码:148 / 152
页数:5
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