Regression Analysis to Create New Truck Trip Generation Equations for Medium Sized Communities
Bibliographic Data
| ID | 19569926 |
|---|---|
| Authors | Mehrnaz Doustmohammadi (University of Alabama in Huntsville), Michael Anderson (0000-0002-4079-1965, University of Alabama in Huntsville, corresponding author), Ehsan Doustmohammadi (Jacksonville University) |
| Year | 2019 |
| Volume | 07 |
| Issue | 03 |
| Pages | 480-491 |
| Publication date | 2019-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Current Urban Studies (JOURNAL) |
| Journal identifiers | ISSN: 2328-4900 • E-ISSN: 2328-4919 |
| Publisher | Scientific Research Publishing, Inc. (PUBLISHER) |
| DOI | 10.4236/cus.2019.73024 |
| OpenAlex | W2975892074 |
| Language | EN |
| References cited | 22 |
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
Data collection · Destinations · Geography · Global Positioning System · Telecommunications · Tourism · Transport engineering · Transportation planning · Trip generation · Truck · Computer Science · Engineering · Transportation and Mobility Innovations · Transportation Planning and Optimization · Urban and Freight Transport Logistics · Automotive Engineering
| Highly cited | No |
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