Uncertainty-Aware Traffic Prediction using Attention-based Deep Hybrid Network with Bayesian Inference

Traffic congestion has an adverse impact on the economy and quality of life and thus accurate traffic flow forecasting is critical for reducing congestion and enhancing transportation management. Recently, hybrid deep-learning approaches show promising contributions in prediction by handling various dynamic traffic features. Existing methods, however, frequently neglect the uncertainty associated with traffic estimates, resulting in inefficient decision-making and planning. To overcome these issues, this research presents an attention-based deep hybrid network with Bayesian inference. The suggested approach assesses the uncertainty associated with traffic projections and gives probabilistic estimates by applying Bayesian inference. The attention mechanism improves the ability of the model to detect unexpected situations that disrupt traffic flow. The proposed method is tested using real-world traffic data from Dhaka city, and the findings show that it outperforms than other cutting-edge approaches when used with real-world traffic statistics.

@article{Rahman2023,
  title     = {Uncertainty-Aware Traffic Prediction using Attention-based Deep Hybrid Network with Bayesian Inference},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Md. Moshiur Rahman and Abu Rafe Md Jamil and Naushin Nower},
  doi       = {10.14569/IJACSA.2023.01406132},
  url       = {https://doi.org/10.14569/IJACSA.2023.01406132}
}