TITLE:
Regression Analysis to Create New Truck Trip Generation Equations for Medium Sized Communities
AUTHORS:
Mehrnaz Doustmohammadi, Michael Anderson, Ehsan Doustmohammadi
KEYWORDS:
Truck Trip Generation Models, Travel Models, Employment Data
JOURNAL NAME:
Current Urban Studies,
Vol.7 No.3,
September
26,
2019
ABSTRACT: This paper uses data from a trucking origin/destination study conducted
with global positioning system (GPS) technology to develop a truck trip
generation model for medium sized urban communities—in this study taken to be
communities between 200,000 and 1,000,000 people. The difficulty with developing
truck trip generation equations centers on the limitation of data. For
passenger transportation, data are collected from household surveys. For truck
transportation, if available, data are typically collected from a small collection
of shippers/businesses within the urban area and extrapolated to cover the
entire study area. Because of the data limitations, truck transportation is
typically indirectly modeled or as an after-thought. Increasing truck volumes,
coupled with cost saving strategies such as just-in-time delivery systems, require
that transportation policymakers analyze infrastructure needs and make
investment decisions that explicitly include truck volumes as a component. This
paper contains a case study using a medium sized urban area and a GPS collected
set of truck origins and destinations to develop a truck specific trip
generation equation using standard employment data. The paper presents the
models developed and validates the models to the case study community. The
paper concludes that the trip generation equations developed can be incorporated
into medium sized community travel models to provide a framework for truck
planning that can be used to improve resource allocation decisions.