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Metaheuristic enabled deep convolutional neural network for traffic flow prediction: Impact of improved lion algorithm
| 题名： | Metaheuristic enabled deep convolutional neural network for traffic flow prediction: Impact of improved lion algorithm |
| 正文语种： | eng |
| 作者： | Monal Patel;Carlos Valderrama;Arvind Yadav |
| 作者单位： | Parul University;University of Mons;Parul University |
| 关键词： | Congestion;DCNN model;error analysis;LN-TU approach;traffic flow |
| 摘要： | Abstract Traffic flow prediction is a basic aspect to be considered in transportation management and modeling. Attaining precise information on near and current traffic flows has an extensive range of appliances and it further aids in managing the congestion. Numerous conventional models failed at offering precise prediction results due to “shallow in architecture and hand engineered in features”. Moreover, the raw traffic flow information contains noise that might lead to the worst prediction results. Therefore, this paper intends to design an enhanced prediction model on traffic flow using Optimized Deep Convolutional Neural Network (DCNN). The input features or the technical indicators subjected to the optimized CNN are Average True Range (ATR), Exponential Moving Average (EMA), Relative Strength Indicator (RSI) and Rate of Change (ROC), respectively. Moreover, for precise prediction, the weights of DCNN are optimally tuned using a new Improved Lion Algorithm (LA) termed as Lion with New Territorial Takeover Update (LN-TU) model. In the end, the betterment of implemented work is compared and proved over the conventional models in terms of error analysis and prediction analysis. |
| 出版年： | 2022 |
| 期刊名称： | Journal of Intelligent Transportation Systems |
| 卷： | 26 |
| 期： | 1/6 |
| 页码： | 735-750 |
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