TrafficBERT: Pre-trained model with large-scale data for long-range traffic flow forecasting

计算机科学 自编码 深度学习 人工智能 概括性 航程(航空) 机器学习 人工神经网络 流量(计算机网络) 嵌入 数据挖掘 计算机安全 心理学 材料科学 复合材料 心理治疗师
作者
Kyohoon Jin,JeongA Wi,Eunju Lee,Shin-Jin Kang,Soo Kyun Kim,Youngbin Kim
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:186: 115738-115738 被引量:36
标识
DOI:10.1016/j.eswa.2021.115738
摘要

Traffic flow prediction has various applications such as in traffic systems and autonomous driving. Road conditions have become increasingly complex, and this, in turn, has increased the demand for effective traffic volume predictions. Statistical models and conventional machine-learning models have been employed for this purpose more recently, deep learning has been widely used. However, most deep learning-based models require data additional to traffic information, such as information on adjacent roads or road weather conditions. Therefore, the effectiveness of these models is typically restricted to certain roads. Even if such information were available, there is a possibility of bias toward a specific road. To overcome this limitation, based on the bidirectional encoder representations from transformers (BERT), we propose trafficBERT, a model that is suitable for use on various roads because it is pre-trained with large-scale traffic data. Our model captures time-series information by employing multi-head self-attention in place of the commonly used recurrent neural network. In addition, the autocorrelation between the states before and after each time step is determined more efficiently via factorized embedding parameterization. Our results indicate that trafficBERT outperforms models trained using data for specific roads, as well as commonly used statistical and deep learning models, such as Stacked Autoencoder, and models based on long short-term memory, in terms of accuracy. • Proposing a deep learning model to predict long-range traffic flow forecasting. • TrafficBERT is a modifies of the BERT structure for traffic flow forecasting. • Increasing the generality of the model by pre-training data on various roads.
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