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

By David Alexander Tedjopurnomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, A. K. Qin. IEEE Transactions on Knowledge and Data Engineering, 2020

442 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/2c8da8267e59e2fec0f9ed12211f1ab33f587be9

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Citations in the corpus

PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction (2023, background): “As a core technology of ITS, traffic flow prediction (Tedjopurnomo et al. 2022) has been widely studied, aiming to predict the future flow of traffic systems”
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “Although there are some surveys [1, 11–19], they only focused on some aspects of traffic prediction, but did not give a complete survey and did not cover most recent works.”
Graph Neural Network for Traffic Forecasting: A Survey (2021, methodology): “After categorizing and discussing 37 deep neural networks for traffic prediction, encoder-decoder LSTM combined with graph-based methods is claimed as the state-of-the-art model in [18].”
Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions (2021, background): “However, some recent advancements, e.g., graph-based deep learning, were not covered in [8].”