Estimating LTL rates using publicly available empirical data

Abstract

We develop a shipper-oriented model to estimate less-than-truckload (LTL) truck rates for transporting goods between origin–destination (O–D) pairs located anywhere in the continental United States. The rate estimate is developed from internet-accessible tariff tables and allows straightforward computation of optimal shipment sizes (minimising total logistics costs) and comparison with the total cost of other modes. The model uses publicly available nominal rates along with a characterisation of the distribution of LTL shipments, based on other publicly available data, to determine a rate that also accounts for the estimated industry average discount from the nominal rate. We use nonlinear regression to build the estimate, with tariff-based rates serving as the dependent variable and load density, shipment weight, and O–D pair distance as the explanatory variables. The model is normalised to reflect average industry rates and current economic conditions using the Producer Price Index for LTL service. Although our results are specific to US markets for truck freight, the method of analysis serves as a model for similar international studies.