Data Science Freight Demand Forecasting

Carriers, freight forwarders, and logistics service providers apply data science techniques—machine learning, time-series models, statistical regression—to estimate future freight volumes from historical shipment data, market indicators, and external variables such as seasonality or economic cycles.