Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention
Spatiotemporal forecasting of traffic flow data represents a typical problem for urban traffic management, involving complex interactions, nonlinearities, and long-range dependencies due to the interwoven nature of the temporal and spatial dimensions. Traditional statistical and machine learning met...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10794773/ |
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| author | Yash Jakhmola Madhurima Panja Nitish Kumar Mishra Kripabandhu Ghosh Uttam Kumar Tanujit Chakraborty |
| author_facet | Yash Jakhmola Madhurima Panja Nitish Kumar Mishra Kripabandhu Ghosh Uttam Kumar Tanujit Chakraborty |
| author_sort | Yash Jakhmola |
| collection | DOAJ |
| description | Spatiotemporal forecasting of traffic flow data represents a typical problem for urban traffic management, involving complex interactions, nonlinearities, and long-range dependencies due to the interwoven nature of the temporal and spatial dimensions. Traditional statistical and machine learning methods struggle to handle both temporal and spatial dependencies in such datasets. While graph convolutional networks and multi-head attention mechanisms have been widely adopted in this field, they often fail to accurately model dynamic temporal patterns and effectively differentiate noise from signals in traffic datasets, leading to potential overfitting. This paper proposes a wavelet-based temporal attention model, namely a wavelet dynamic spatiotemporal aware graph neural network (W-DSTAGNN), for tackling the traffic forecasting problem. A key feature of W-DSTAGNN is the use of wavelets that can effectively separate noise and capture multi-resolution temporal patterns. Wavelet decomposition can help by decomposing the signal into components that can be analyzed independently, reducing the impact of non-stationarity and handling long-range dependencies of traffic flow datasets. These enable our proposal to generate more robust and accurate forecasts of complex and dynamic traffic dependencies than commonly used spatiotemporal deep learning models. Benchmark experiments using three popularly used statistical metrics confirm that our proposal efficiently captures spatiotemporal correlations and outperforms eleven state-of-the-art models (including both temporal and spatiotemporal benchmarks) on three publicly available traffic datasets. Our proposed approach can better handle dynamic temporal and spatial dependencies, delivering reliable long-term forecasts, and generating interval forecasts to enhance probabilistic forecasting of traffic datasets. |
| format | Article |
| id | doaj-art-b270aa6ee4024c6eafe765374d6d89a5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b270aa6ee4024c6eafe765374d6d89a52024-12-21T00:01:10ZengIEEEIEEE Access2169-35362024-01-011218879718881210.1109/ACCESS.2024.351619510794773Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal AttentionYash Jakhmola0https://orcid.org/0009-0005-1068-886XMadhurima Panja1https://orcid.org/0009-0004-7467-2456Nitish Kumar Mishra2https://orcid.org/0009-0009-1088-7139Kripabandhu Ghosh3https://orcid.org/0000-0002-8130-1221Uttam Kumar4https://orcid.org/0000-0002-0822-7993Tanujit Chakraborty5https://orcid.org/0000-0002-3479-2187Department of Mathematics and Statistics, Indian Institute of Science, Education and Research, Kolkata, IndiaCenter for Data Science, International Institute of Information Technology, Bangalore, Bengaluru, IndiaDepartment of Mathematics and Statistics, Indian Institute of Science, Education and Research, Kolkata, IndiaDepartment of Computational and Data Sciences, Indian Institute of Science, Education and Research, Kolkata, IndiaCenter for Data Science, International Institute of Information Technology, Bangalore, Bengaluru, IndiaSAFIR, Sorbonne University Abu Dhabi, Abu Dhabi, United Arab EmiratesSpatiotemporal forecasting of traffic flow data represents a typical problem for urban traffic management, involving complex interactions, nonlinearities, and long-range dependencies due to the interwoven nature of the temporal and spatial dimensions. Traditional statistical and machine learning methods struggle to handle both temporal and spatial dependencies in such datasets. While graph convolutional networks and multi-head attention mechanisms have been widely adopted in this field, they often fail to accurately model dynamic temporal patterns and effectively differentiate noise from signals in traffic datasets, leading to potential overfitting. This paper proposes a wavelet-based temporal attention model, namely a wavelet dynamic spatiotemporal aware graph neural network (W-DSTAGNN), for tackling the traffic forecasting problem. A key feature of W-DSTAGNN is the use of wavelets that can effectively separate noise and capture multi-resolution temporal patterns. Wavelet decomposition can help by decomposing the signal into components that can be analyzed independently, reducing the impact of non-stationarity and handling long-range dependencies of traffic flow datasets. These enable our proposal to generate more robust and accurate forecasts of complex and dynamic traffic dependencies than commonly used spatiotemporal deep learning models. Benchmark experiments using three popularly used statistical metrics confirm that our proposal efficiently captures spatiotemporal correlations and outperforms eleven state-of-the-art models (including both temporal and spatiotemporal benchmarks) on three publicly available traffic datasets. Our proposed approach can better handle dynamic temporal and spatial dependencies, delivering reliable long-term forecasts, and generating interval forecasts to enhance probabilistic forecasting of traffic datasets.https://ieeexplore.ieee.org/document/10794773/Traffic forecastingwavelet transformationtemporal attentionspatiotemporal datacomputational complexity |
| spellingShingle | Yash Jakhmola Madhurima Panja Nitish Kumar Mishra Kripabandhu Ghosh Uttam Kumar Tanujit Chakraborty Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention IEEE Access Traffic forecasting wavelet transformation temporal attention spatiotemporal data computational complexity |
| title | Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention |
| title_full | Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention |
| title_fullStr | Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention |
| title_full_unstemmed | Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention |
| title_short | Spatiotemporal Forecasting of Traffic Flow Using Wavelet-Based Temporal Attention |
| title_sort | spatiotemporal forecasting of traffic flow using wavelet based temporal attention |
| topic | Traffic forecasting wavelet transformation temporal attention spatiotemporal data computational complexity |
| url | https://ieeexplore.ieee.org/document/10794773/ |
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