A Frequency-Aware Spatio-Temporal Network for Traffic Flow Prediction

Shunfeng, Peng; Shen, Yanyan; Zhu, Yanmin; Chen, Yuting

doi:10.1007/978-3-030-18579-4_41

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…Peng et al [ 33 ] developed a frequency-aware spatiotemporal network (FASTNet) for traffic flow forecasting. The spectrum of a traffic flow series that reflects certain travel patterns of passengers was obtained by applying the discrete Fourier transform (DFT).…”

Section: Decomposition-reconstruction-based Hybrid Modelsmentioning

confidence: 99%

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

Chen

Wang

Hua

et al. 2022

Sensors

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Traffic state prediction provides key information for intelligent transportation systems (ITSs) for proactive traffic management, the importance of which has become the reason for the tremendous number of research papers in this field. Over the last few decades, the decomposition-reconstruction (DR) hybrid models have been favored by numerous researchers to provide a more robust framework for short-term traffic state prediction for ITSs. This study surveyed DR-based works for short-term traffic state forecasting that were reported in the past circa twenty years, particularly focusing on how decomposition and reconstruction strategies could be utilized to enhance the predictability and interpretability of basic predictive models of traffic parameters. The reported DR-based models were classified and their applications in this area were scrutinized. Discussion and potential future directions are also provided to support more sophisticated applications. This work offers modelers suggestions and helps to choose appropriate decomposition and reconstruction strategies in their research and applications.

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Section: Decomposition-reconstruction-based Hybrid Modelsmentioning

confidence: 99%

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

Chen

Wang

Hua

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The problem has also been formulated as a sequence-to-sequence prediction, by exploiting generative adversarial networks to perform multi-step spatio-temporal crowd flow prediction at a city-wide level [83]. Finally, an original approach is given by the work in [84] which proposes a frequency-aware spatio-temporal convolutional neural network. Such network has various kernels of different sizes which can model the temporal correlations with multi-scale frequencies.…”

Section: Deep Learning For Time-series Forecastingmentioning

confidence: 99%

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

Cecaj

Lippi

Mamei

et al. 2021

IoT

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The possibility of sensing and predicting the movements of crowds in modern cities is of fundamental importance for improving urban planning, urban mobility, urban safety, and tourism activities. However, it also introduces several challenges at the level of sensing technologies and data analysis. The objective of this survey is to overview: (i) the many potential application areas of crowd sensing and prediction; (ii) the technologies that can be exploited to sense crowd along with their potentials and limitations; (iii) the data analysis techniques that can be effectively used to forecast crowd distribution. Finally, the article tries to identify open and promising research challenges.

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“…Also, it failed to incorporate spatial and temporal correlations among roadways, which are critical for prediction performance. Another work [32] also considered heterogeneous frequency in traffic flow prediction, by dynamically filtering the inputs through Discrete Fourier Transform (DFT) on traffic flow data. With the filtered tensors, a 3D convolutional network is designed to extract the spatiotemporal features automatically, and several kernels with various sizes on the temporal dimension are employed to model the temporal correlations with multi-scale frequencies.…”

Section: Related Workmentioning

confidence: 99%

Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency

Sun¹,

Jiang²,

Lam³

et al. 2021

Preprint

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Substantial efforts have been devoted to the investigation of spatiotemporal correlations for improving traffic speed prediction accuracy. However, existing works typically model the correlations based solely on the observed traffic state (e.g. traffic speed) without due consideration that different correlation measurements of the traffic data could exhibit a diverse set of patterns under different traffic situations. In addition, the existing works assume that all road segments can employ the same sampling frequency of traffic states, which is impractical. In this paper, we propose new measurements to model the spatial correlations among traffic data and show that the resulting correlation patterns vary significantly under various traffic situations. We propose a Heterogeneous Spatial Correlation (HSC) model to capture the spatial correlation based on a specific measurement, where the traffic data of varying road segments can be heterogeneous (i.e. obtained with different sampling frequency). We propose a Multi-fold Correlation Attention Network (MCAN), which relies on the HSC model to explore multi-fold spatial correlations and leverage LSTM networks to capture multi-fold temporal correlations to provide discriminating features in order to achieve accurate traffic prediction. The learned multi-fold spatiotemporal correlations together with contextual factors are fused with attention mechanism to make the final predictions. Experiments on real-world datasets demonstrate that the proposed MCAN model outperforms the state-of-the-art baselines.

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A Frequency-Aware Spatio-Temporal Network for Traffic Flow Prediction

Cited by 9 publications

References 17 publications

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency

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