Thesis/Capstone
Publication Date
Authored by
David Sokoloff, Gaohui Zhang
Advisor(s): Chris Caplice
Topic(s) Covered:
  • Transportation
Abstract

The trucking industry is crucial to the United States economy. An overwhelming majority of goods transported across the US are moved in trucks. For most companies, truck transportation is a prominent component that impacts their production, warehousing, customer service, and overall business performance. In fact, trucking constitutes one of the largest operational costs for a company. Trucking costs are highly volatile due to their association with the capricious freight industry and the US economy. Unexpected market fluctuations inevitably disturb companies’ budget planning and operations, as well as impact their profits. This paper formulates a machine learning model to predict the US truckload dry van spot rate and a playbook of contingent actions. The model variables target and recognize the key elements in the trucking industry and the economy. Tested across 6 years of data, the model achieved an average MAPE below 7% and mean error below 0.05 for predicting 12 months in the future. The strong forecast accuracy allows companies to employ our playbook’s strategic and tactical measures to mitigate risk and unplanned costs stemming from the volatility in the US trucking market.

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