Logistics planning strategies for seasonal traffic management

Background: Seasonal urban traffic, driven by holidays, tourism, and weather conditions, poses significant challenges to logistics planning by exacerbating congestion and disrupting supply chains. Addressing these issues requires advanced forecasting techniques and integrated management strategies. This study explores the use of Exponential Triple Smoothing (ETS) forecasting to model traffic variations and develop practical strategies for managing seasonal urban traffic. Methods: A 23-year dataset (July 2000–July 2023) capturing monthly traffic volumes for heavy goods vehicles (HGVs), cars with trailers, and buses was analysed. ETS was applied to a conceptual scenario designed to demonstrate the application of real-world traffic data. This scenario illustrates how forecasting techniques can be used to analyse traffic trends and assess the impact of seasonal variations. Forecast accuracy was evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) to validate the reliability of the ETS model within the context of the KC Bridge scenario. Results: The forecasts revealed significant seasonal peaks in leisure traffic and steady growth in HGV volumes, with total traffic projected to exceed the sustainable threshold of 100,000 vehicles/month by 2025 during peak periods. Post-COVID variability in traffic patterns highlighted the challenges of using exceptional data for planning. Recommendations include dynamic toll pricing, infrastructure upgrades, and predictive scheduling to mitigate congestion and optimise logistics operations. Conclusions: By integrating ETS forecasts with strategic interventions, this study provides actionable insights for urban logistics and infrastructure planning. The KC Bridge case study provides a conceptual framework for addressing seasonal traffic challenges, offering replicable strategies for sustainable urban transportation systems.