Wavelet-based Temporal Attention Improves Traffic Forecasting
- URL: http://arxiv.org/abs/2407.04440v1
- Date: Fri, 5 Jul 2024 11:42:39 GMT
- Title: Wavelet-based Temporal Attention Improves Traffic Forecasting
- Authors: Yash Jakhmola, Nitish Kumar Mishra, Kripabandhu Ghosh, Tanujit Chakraborty,
- Abstract summary: Forecasting of traffic flow data represents a typical problem in the field of machine learning, impacting urban traffic management systems.
Traditional statistical and machine learning methods cannot adequately handle both the temporal and spatial dependencies in these complex traffic flow datasets.
This paper proposes a wavelet-based temporal attention model, namely a wavelet-based dynamic processing-temporal aware graph neural network (WDSNN), for tackling the traffic forecasting problem.
Our ensemble data-driven method can handle dynamic temporal and spatial dependencies and make-term forecasts in an efficient manner.
- Score: 3.131352561462676
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Spatio-temporal forecasting of traffic flow data represents a typical problem in the field of machine learning, impacting urban traffic management systems. Traditional statistical and machine learning methods cannot adequately handle both the temporal and spatial dependencies in these complex traffic flow datasets. A prevalent approach in the field is to combine graph convolutional networks and multi-head attention mechanisms for spatio-temporal processing. This paper proposes a wavelet-based temporal attention model, namely a wavelet-based dynamic spatio-temporal aware graph neural network (W-DSTAGNN), for tackling the traffic forecasting problem. Benchmark experiments using several statistical metrics confirm that our proposal efficiently captures spatio-temporal correlations and outperforms ten state-of-the-art models on three different real-world traffic datasets. Our proposed ensemble data-driven method can handle dynamic temporal and spatial dependencies and make long-term forecasts in an efficient manner.
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