Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions
By Xueyan Yin, Genze Wu, Jinze Wei, Yanming Shen, Heng Qi, Baocai Yin. IEEE transactions on intelligent transportation systems (Print), 2021
438 citations recorded by the source corpus.
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been devoted to this area, especially deep learning method, greatly advancing traffic prediction abilities. The purpose of this paper is to provide a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives. Specifically, we first summarize the existing traffic prediction methods, and give a taxonomy. Second, we list the state-of-the-art approaches in different traffic prediction applications. Third, we comprehensively collect and organize widely used public datasets in the existing literature to facilitate other researchers. Furthermore, we give an evaluation and analysis by conducting extensive experiments to compare the performance of different methods on a real-world public dataset. Finally, we discuss open challenges in this field.
Subfields: Building and Construction, Control and Systems Engineering, Transportation
View Paper: https://www.semanticscholar.org/paper/2bc2360197ed281726e6eb67b09fe7fd17241c9a
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Citations in the corpus
- PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction (2023, background): “For traffic flow prediction, the fundamental challenge is to effectively capture and model the complex and dynamic spatial-temporal dependencies of traffic data (Yin et al. 2022).”
- Graph Neural Network for Traffic Forecasting: A Survey (2021, methodology): “, 2020), while the others prefer to compare them with the statistics-based and machine learning methods (Manibardo et al., 2021; Yin et al., 2021).”
- TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting (2024, background): “…applications, such as economics (Granger & Newbold, 2014), energy (Martín et al., 2010; Qian et al., 2019), traffic planning (Chen et al., 2001; Yin et al., 2021) and weather prediction (Wu et al., 2023b), which is to predict future temporal variations based on past observations of time series…”