超参数
计算机科学
差异进化
流量(计算机网络)
深度学习
趋同(经济学)
人工智能
智能交通系统
时间序列
人工神经网络
进化算法
机器学习
数据挖掘
土木工程
计算机安全
工程类
经济
经济增长
作者
Xiaocai Zhang,Zhixun Zhao,Jinyan Li
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-02-01
卷期号:10 (3): 2391-2403
被引量:3
标识
DOI:10.1109/jiot.2022.3212056
摘要
Accurate and reliable traffic flow prediction is difficult due to the highly nonlinear, complex, and stochastic natures of urban traffic flow data, but its solutions are critically important for intelligent transportation systems (ITSs) and Internet of Things (IoT). In this study, a novel deep learning framework, named adaptive reinitialized differential evolution (ARDE)-neural basis expansion analysis for time-series forecasting (N-BEATS), is proposed to address this challenge. With the framework of ARDE-N-BEATS, first, an N-BEATS-based deep learning architecture is formulated for modeling traffic flow data. Second, a novel enhanced evolutionary algorithm, termed ARDE, is presented for optimizing the hyperparameter and structure of N-BEATS. Compared to the vanilla differential evolution (DE) algorithm, ARDE exhibits faster convergence and stronger searching capabilities. Experiments on three real-world traffic flow data sets from Dublin and San Francisco demonstrate that ARDE-N-BEATS can achieve high accuracy of at least 94% for most of the predictions, and outperforms the existing counterpart methods. A comparison between different hyperparameter optimization approaches further reveals that ARDE provides better or very competitive predictions and saves as high as 78.90% of computational expense.
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