Reading the freight forecast: Making sense of the truckload market cycle

By Dr. Chris Caplice, Dr. Angi Acocella, Sarah Roman and Wirinratch (Bonus) Kirirak

Editor's note: The SCM thesis Reading the Freight Forecast: Making Sense of the Truckload Market Cycle was authored by Sarah Roman and Wirinratch (Bonus) Kirirak, and supervised by Dr. Chris Caplice ([email protected]) and Dr. Angi Acocella ([email protected]). For more information on this research, please contact the thesis supervisors.

The U.S. dry van full truckload (FTL) market is a balancing act between truck capacity (supply) and freight demand. This dynamic was shaped largely by the Motor Carrier Act of 1980, which deregulated the industry and lowered barriers to entry. Deregulation introduced greater price volatility, creating a cyclical pattern that alternates between tight and soft market conditions. Though many in the industry have worked to make sense of these cycles, no shared definition or forecasting framework existed. This study addresses this gap by proposing an industry-driven definition of the truckload market cycle and identifying influential variables to forecast the timing of future phase shifts using statistical modeling.

Defining the cycle, forecasting the future

Given the lack of a consensus-based definition of the truckload market cycle, and with sponsorship from C.H. Robinson, we developed a clear, structured framework to define its phases and enable more effective forecasting. We conducted 20 interviews with experts across the industry, revealing key external factors shaping the market. These insights informed a broader industry-wide survey to validate and expand the findings. Spot and contract rates emerged as the most widely used indicators of market conditions and were chosen as dependent variables for forecasting. Based on this input, we defined a four-phase cycle definition represented by spot rate, contract rate, and spot-premium ratio.

From this process, 31 potential influencing variables were identified and evaluated for their relationship to spot and contract rates. Statistical testing found 12 metrics to be significant, including inventory to sales ratio, housing starts, Producer Price Index (PPI), diesel prices, Commodity Research Bureau (CRB) and Bloomberg Commodity indices, and Class 8 indicators. To evaluate the model, we introduced a “timing error” metric that measures how closely predicted phase shifts aligned with actual market transitions. These insights can help stakeholders anticipate changes in capacity and demand, enabling tactical and strategic adjustments in response to external shifts.

Reading the road ahead

The solution developed includes two core components: a framework defining the truckload market cycle and a forecasting model that uses macroeconomic and industry indicators to anticipate phase shifts.

In long-term forecasts, the most accurate model, using spot and contract rates, predicted market shifts with an average timing error of 4.4 months in advance of the actual shift. Models incorporating Class 8 cancellations or housing starts achieved similar accuracy. We also applied a short-term forecasting method focused solely on the next phase shift, which resulted in a timing error ranging from zero to two months. 

Models using spot rate, contract rate, Class 8 cancellations, and housing starts had the lowest timing errors. Still, metrics like inventories to sales ratio, PPI, diesel prices, commodity indices, and other Class 8 indicators remain critical to understanding market dynamics.

Ultimately, this research reveals three critical insights:

1. Spot and contract rates are the most used indicators of truckload market conditions by industry experts.
2. Changes in production activity, Class 8 trucking data, and commodity indices offer directional insights on where spot and contract rates may move.
3. Although some leading indicators show strong predictive power, precisely forecasting the timing of truckload market cycle shifts remains challenging.

Understanding and anticipating truckload market cycles is key for shippers, carriers, and brokers to stay ahead of disruptions. This research offers practical value during Request for Proposals (RFP) and contract negotiations by helping align terms with market trends and set more strategic contract lengths. It supports carriers in optimizing networks, enables shippers to contract more effectively, and helps brokers strengthen relationships and profitability. We share this work as a foundation for continued industry exploration and more informed decision-making.

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