Robust evaluation of big data-driven winter weather traffic models using six weigh-in-motion sites as testbeds in Alberta's highway network

This study assesses the temporal transferability of winter traffic models using dummy variable regression at six Weigh-in-Motion (WIM) sites on Alberta's highway network. Models for two vehicle classes were developed using five years of traffic and weather data. To evaluate transferability, an additional year of data was collected, and two model structures—dummy variable and naive—were tested. The models’ estimation accuracy was measured using R² and five error metrics. Results indicate successful transferability of the models to a different year, with performance varying by road function and vehicle class. The study highlights that different model types might be needed for each vehicle class to ensure high temporal transferability. Additionally, the quality of the test data is crucial for obtaining reliable transfer results. This research addressed gaps in previous studies by collectively testing the temporal transferability of winter traffic models across an entire highway network.