Forecasting Freeway Traffic Volumes with Adverse Weather

Consequently, the accurate prediction of traffic volume is vital. This study proposes an adverse weather traffic volume prediction model combining Convolution Neural Networks, Bi-directional Long Short-Term Memory, and Attention Mechanism.

The 5-minute highway traffic volume data from December 1, 2021, to March 13, 2022, in Minnesota, U.S.A, and the weather data in the same period provided by Meso West were used as the experimental data. The proposed model was compared with three single prediction models, two validated hybrid models, and the model itself without integrating weather factors. The experiments show that the prediction accuracy of the proposed model is higher than other comparison models.