A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges

深度学习 计算机科学 数据科学 人气 领域(数学) 人工智能 人工神经网络 自回归积分移动平均 灵活性(工程) 机器学习 时间序列 心理学 数学 社会心理学 统计 纯数学
作者
David Alexander Tedjopurnomo,Zhifeng Bao,Baihua Zheng,Farhana M. Choudhury,A. K. Qin
出处
期刊:IEEE Transactions on Knowledge and Data Engineering [IEEE Computer Society]
卷期号:: 1-1 被引量:245
标识
DOI:10.1109/tkde.2020.3001195
摘要

In this modern era, traffic congestion has become a major source of severe negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The research field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Recently, researchers have started to focus on machine learning models because of their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its sheer prediction power which can be attributed to the complex and deep structure. Despite the popularity of deep neural network models in the field of traffic prediction, literature surveys of such methods are rare. In this work, we present an up-to-date survey of deep neural network for traffic prediction. We will provide a detailed explanation of popular deep neural network architectures commonly used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field.
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