This work focuses on predicting traffic flow on the Lions Gate Bridge adjacent to Stanley Park in Vancouver, Canada, employing a novel hybrid model. The bridge serves as a vital route for commuters from northern Vancouver to the city center, experiencing substantial daily traffic volume, estimated at 60,000+ vehicles on workdays, leading to peak congestion during morning and afternoon commutes. Therefore, the urgency to establish a traffic prediction model is paramount. The aim is to address this issue, providing urban planners with insights for more effective traffic planning near the Lions Gate Bridge to alleviate congestion. To that end, in this work, we propose a novel data-driven hierarchical forecast combination model to enhance the accuracy of traffic flow predictions. Traffic-related data for this project are sourced from the Ministry of Transportation and Infrastructure of BC, while climate-related datasets are obtained from Environment Canada. The results demonstrate the better performance of the proposed model compared to conventional forecasting models.
