Details:

Creators:
Lawrence, K ; Kleinman, G
Lawrence, K ; Kleinman, G Less -
Corporate Creators:
New Jersey Institute of Technology
Subject/TRT Terms:
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Appropriations Budgeting Computer Models Decision Support Systems Economic Growth Environment Mobility National Security Population Regression Analysis Safety Socioeconomic Factors Supply Traffic Flow Traffic Forecasting Trucking Truck Traffic
Resource Type:
Research Paper
Corporate Publisher:
New Jersey. Dept. of Transportation
Abstract:
This research develops a regression-based model for forecasting truck borne freight in the continental United States. This model is capable of predicting freight commodity flow information via trucks to assist transportation planners who wish to understand when and where new road facilities are needed. Such an understanding is important because shipments by truck account for 53% of total tonnage shipped within the U.S. and 72% of total shipments for value (Chin, Hopson & Hwang, 1998). The methods used here can be generalized to other transportation modalities. When, as was done here, this model is allied with databases of forecast economic and population data, it can be used to forecast future truck freight flows. This research begins with the use of a traditional gravity model to predict freight flow within the states of the continental United States. Such a model posits that freight volume between any two areas is a direct function of the attraction of each area and inversely proportional to the distance between the two areas. Obviously, the populations of the destination and origin states serve as one possible measure of their demand for, and ability to supply, goods and services. The greater the distance between the destination and origin states, however, the less likely that freight will move between them since shipment costs will be higher. Population alone, however, has certain limitations as an indicator of the power of a region to draw freight flows from any other area since the purchasing power of the population may be low. In order to increase the model's predictive ability, several socioeconomic variables were included. These include each region's total employment, earned income, and total personal income.
Format:
PDF
Funding:
NCTIP97-21
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:c1083e592d391d54fe894dd6e013e2258c87c5ca5a4f7892afa4bc1241e41575
Download URL:
https://rosap.ntl.bts.gov/view/dot/4795/dot_4795_DS1.pdf
File Type:

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