M<sup>3</sup>AN: Multitask Multirange Multisubgraph Attention Network for Condition-Aware Traffic Prediction

Luo, Dan; Zhang, Dong; Cao, Zijian; Wu, Mingyao; Liu, Liang; Ma, Huadóng

doi:10.1109/tits.2022.3216678

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…Then, the fuzzy entropy is used to calculate the similarity magnitude between adjacent component sequences, which realizes the division between k * lowfrequency and K − k * reconstructed high-frequency components. Finally, the central prediction node p's modal component sequences vimf 1 p , • • • , vimf k * p , vimf h p and its first-order neighborhood node q's modal component sequences vimf 1 q , • • • , vimf k * q , vimf h q are obtained. The improved traffic flow VMD module based on fuzzy entropy will be explicitly described in Section IV.…”

Section: A Vmd Modulementioning

confidence: 99%

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ST-VGBiGRU: A Hybrid Model for Traffic Flow Prediction With Spatio-Temporal Multimodality

Yin

Liu

et al. 2023

IEEE Access

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Traffic flow prediction is a key step to realizing the effective guidance and control of intelligent transportation systems. For using the short-term non-stationarity and spatio-temporal correlation presented in traffic flow, a spatio-temporal hybrid prediction model, which called ST-VGBiGRU, based on improved Variational Modal Decomposition (VMD), Graph Attention Network (GAT), and Bidirectional Gated Recurrent Unit (BiGRU) network is proposed. First, the traffic flow sequence is decomposed into a series of relatively stationary modal components using VMD algorithm to reduce its short-term nonstationarity. The high-frequency modal components are noise-reduced using the Fuzzy Entropy (Fuzzy En) method to improve the accuracy of decomposition. After that, the GAT network is used to capture the different attention levels of the prediction node to their neighboring traffic nodes, which obtain more spatial characteristics of traffic flow. Then, each modal component containing spatial features is fed into the BiGRU network separately to capture its temporal correlation. Each model parameter is trained to the optimum using the improved RMSProp algorithm, which improves the model's prediction accuracy while speeding up the convergence of RMSProp algorithm. In order to illustrate the performance of the ST-VGBiGRU model, the RTMC traffic dataset is used to conduct ablation experiments on the improved VMD module, the GAT module, and the improved BiGRU module. Meanwhile, we combined the PeMS traffic dataset to conduct baseline experiments and multi-step prediction experiments with the other six models. The results show that the prediction performance of our model is better than all the other baseline models.

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Section: A Vmd Modulementioning

confidence: 99%

“…transportation systems has increased dramatically [1]. Promoting intelligent road management and traffic signal linkage has become a key area of China's 14th Five-Year Plan [2].…”

Section: Introductionmentioning

confidence: 99%

ST-VGBiGRU: A Hybrid Model for Traffic Flow Prediction With Spatio-Temporal Multimodality

Yin

Liu

et al. 2023

IEEE Access

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“…The model uses bidirectional attention flows to investigate the intricate spatiotemporal correlations between the prediction data and historical data, predicting real-time ride-hailing demand during the COVID-19 pandemic. Luo et al [35] have developed a Multi-Task Multi-Range Multi-Subgraph Attention Network (M3AN) which can be adaptable for both normal and abnormal traffic situations. Different subgraphs are used to model node attributes while attention mechanisms are employed to measure the dynamic spatial correlations.…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Prediction with Random Walks on Graph and Spatiotemporal Bidirectional Attention Transformer

Yang,

Zhou,

2024

Applied Sciences

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Traffic flow prediction is crucial in intelligent transportation systems. Considering the severe disruptions caused by traffic accidents or congestion, a time series model is developed for traffic flow prediction based on multiple random walks on graphs (MRWG) and the bidirectional spatiotemporal attention mechanism (BSAM), which can adapt to both normal and exceptional situations. The MRWG mechanism is applied to capture spatial features of urban areas during traffic accidents and congestion, especially the spatial dependencies among neighboring regions. Further, a local position attention module is applied to acquire the spatial correlations between different regions to investigate their impact on the global area, while a local temporal attention module is adopted to extract short-term periodic time correlations from traffic flow data. Finally, a spatiotemporal bidirectional attention module is applied to simultaneously extract both the temporal and spatial correlations of the historical traffic flow data in order to generate the output prediction. Experiments have been conducted on NYCTaxi and NYCBike datasets with abnormal events, and the results indicate that the developed model can efficiently predict traffic flow in abnormal events, especially short-term traffic disruptions, outperforming the baseline methods under both abnormal and normal conditions.

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An Efficient Skip Link-based Traffic Prediction Algorithm with Multi-Scale Feature Extraction

Liu,

Li,

et al. 2023

2023 19th International Conference on Mobility, Sensing and Networking (MSN)

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M³AN: Multitask Multirange Multisubgraph Attention Network for Condition-Aware Traffic Prediction

Cited by 4 publications

References 43 publications

ST-VGBiGRU: A Hybrid Model for Traffic Flow Prediction With Spatio-Temporal Multimodality

ST-VGBiGRU: A Hybrid Model for Traffic Flow Prediction With Spatio-Temporal Multimodality

Traffic Flow Prediction with Random Walks on Graph and Spatiotemporal Bidirectional Attention Transformer

An Efficient Skip Link-based Traffic Prediction Algorithm with Multi-Scale Feature Extraction

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M3AN: Multitask Multirange Multisubgraph Attention Network for Condition-Aware Traffic Prediction

Cited by 4 publications

References 43 publications

ST-VGBiGRU: A Hybrid Model for Traffic Flow Prediction With Spatio-Temporal Multimodality

ST-VGBiGRU: A Hybrid Model for Traffic Flow Prediction With Spatio-Temporal Multimodality

Traffic Flow Prediction with Random Walks on Graph and Spatiotemporal Bidirectional Attention Transformer

An Efficient Skip Link-based Traffic Prediction Algorithm with Multi-Scale Feature Extraction

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Product

Resources

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M³AN: Multitask Multirange Multisubgraph Attention Network for Condition-Aware Traffic Prediction