Forecasting freight demand using economic indices

Abstract

This paper describes the results of an effort to predict future freight volume in the truckload (TL) trucking industry. The approach involves the use of stepwise multiple linear regression models that relate freight volume to a variety of economic indicators. The models are built using a large set of actual freight data provided by J.B. Hunt Transport (JBHT), one of the world’s largest TL carriers. The data was first analyzed using the overall set of national data, and then for specific industrial and regional segments. The overall results of these analyses should prove useful to a wide variety of transportation and logistics operations.

Radial Basis Function Neural Network with Particle Swarm Optimization Algorithms for Regional Logistics Demand Prediction

“…Moving average [24] Suitable for spot prediction Exponential smoothing [25] Repeated prediction with or without a change in seasons Grey model [6] Development of time series showing exponential trend Elastic coefficient method [26] Two factors between and have an exponential relationship Linear regression model [3] A linear relationship between independent variables and dependent variables Artificial neural network model [7] Being able to make prediction of nonlinear corresponding relationship Time series forecasting method does not consider the influence of other economic development indicators. It disregards that the regional logistics demand is of derivation.…”

“…It also provides support for the construction of logistics infrastructure. Scholars at home and around the world established more predictive models for macrologistics needs, for example, the space-time multinomial probity model of forecasting freight transportation [1], nonlinear air services demand model based on time series [2], stepwise linear regression method for cargo forecasting [3], logistics demand analysis model combining input-output and spatial price [4], route comparison model and gravity model [5], grey prediction model, fuzzy forecast and neural network prediction model, and so forth [6][7][8]. Yet, few scholars combined regional economic development with regional logistics demand forecasts closely together.…”

Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession

“…Moving averages are often used to isolate the trend. We propose, however, to extract the trend directly from the original data with both the polynomial and logistic curve models and then to decompose the combination of the cycle and seasonality by the additive model in (5). Considering the close relationship between transportation and economics, we can ignore the cyclical component by taking the seasonality as a cycle of 12 months.…”

“…Realistically, business owners estimate the service demand from their past experiences which can be wrong or misleading. Almost all freight demand analysis usually accounts for a whole country, a region, or a corridor between cities by integration or by mode and is usually to do with public transportation planning [4][5][6]. Two previous research articles which examined SMTEs' demand forecasting tackled less than truckload (LTL) for both short-term and long-term forecasting.…”

Transportation Energy Futures Series: Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future

“…The report concludes with a suggestion that adding a tour-based model capability to capture service vehicles and local pickup and delivery activities would be a desirable feature for future development. Fite et al (2002). "Forecasting Freight Demand Using Economic Indices."…”