Estimating the demand for vehicle-miles-traveled using household survey data
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Estimating the demand for vehicle-miles-traveled using household survey data results from the 1990 Nationwide Personal Transportation Survey by Margaret A. Walls

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Published by Resources for the Future in Washington, DC .
Written in English

Subjects:

  • Transportation, Automotive--United States.

Book details:

Edition Notes

Proceedings of a workshop held in Washington, D.C., June 14-15, 1988.

Statementby Margaret A. Walls, Alan J. Krupnick, and H. Carter Hood.
The Physical Object
Pagination26 p. $77.95 C.1.
Number of Pages95
ID Numbers
Open LibraryOL17590457M

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of this research is to examine how vehicle miles traveled, fuel consumption and emissions may be reduced through shifts in vehicle type choice behavior. Using the National Household Travel Survey data it is possible to develop a model to estimate household travel demand . recommend the use of ground count-based programs for the estimation of vehicle-miles traveled. The Highway Performance Monitoring System (HPMS), which is being used by State DOTs, is a program developed by the FHWA for the monitoring of the nation’s highway infrastructure. INDOT is . Uses of National Household Travel Survey Data Statistical Evidence of the Impact of Air Quality Control on Vehicle Miles Traveled and Vehicle Ownership from and NHTS Data 15 Using the U.S. National Household Travel Survey to estimate the impact of passenger. Using household data from the National Household Travel Survey (NHTS), I estimate the effects on VMT of gasoline prices and fuel economy. The dependent variable is VMT by vehicle, and the independent variables include the current gasoline price, the vehicle’s fuel economy, and household and vehicle characteristics. I.

Chapter 2 discusses the general econometric and statistical issues that arise when using survey data for estimation and inference. Chapter 3 discusses the use of survey data to measure welfare, poverty, and distribution. Chapter 4 discusses the use of household budget data to explore patterns of household demand. Chapter 2 discusses the general econometric and statistical issues that arise when using survey data for estimation and inference. Chapter 3 covers the use of survey data to measure welfare, poverty, and distribution. Chapter 4 focuses on the use of household budget data to explore patterns of household demand. Journals & Books; Help Download PDF Download. Share. Export. Advanced. Energy Policy. Vol October , Pages Estimation of urban residential electricity demand in China using household survey data. Author links open overlay panel Shaojie Zhou a Fei Teng b. Show more.   A full implementation of a travel demand estimation model using big data. • Methods to denoise trajectory data, estimate OD matrices, and route trips. • A modular, efficient, generalized system architecture for easy portability to many cities. • An interactive visualization platform to explore and communicate results.

  To help users with the interpretation of the results some examples are given in chapter 3. Chapter 4 of the book provides guidelines for the preparation of the input datasets. Finally, chapter 5 explains how to use the software. Both the software and this book are products of decades of experience in analyzing food security. The data come from the Nationwide Personal Transportation Survey. It is found that density matters, but not much. A 10 percent increase in density leads to only a percent reduction in household automobile travel. By comparison, a 10 percent increase in household income leads to a 3 percent increase in automobile travel.   Vehicle miles traveled (VMT) is one of the basic metrics with applications in almost every aspect of traffic engineering and safety. Traditionally, there are many methods available to estimate the VMT, e.g., traffic volume counts, odometer readings, household questionnaire surveys, and fuel sale data. Evaluation Methods for Estimating Vehicle Miles Traveled with GPS Travel Survey Data. This research investigated the degree to which traditional routing algorithms, including those that took congestion levels into consideration, could be used to accurately predict GPS-recorded vehicle miles traveled (VMT) if only activity locations were known.