Document Type

Report

Publication Date

1-2014

Subjects

Travel -- United States -- Data collection, Household surveys, Travel -- United States -- Statistics, Perturbation (Mathematics)

Abstract

Public agencies spend vast amounts of money collecting information about passenger travel in household travel surveys. These data are valuable for the rich and detailed information they provide, which contribute to regional and statewide travel demand models. These data have utility beyond travel demand modeling in their application to transportation policy and travel behavior research. As the demand on these data increase, so have the quantity of information collected. Detailed geospatial referencing of the home, work and other travel destinations are common practice and permit the integration with other spatially archived data sources, such as land use characteristics, transportation system information, and other built environment, social and economic data. Other public agencies, private consultancies, non-profits and educational institutions may benefit from access to the original data with applications to areas such public health, equity, transportation safety and urban planning. Oregon Modeling Collaborative (OMC) has entered into an agreement with Oregon Modeling Steering Committee (OMSC) to host and make available Oregon Household Activity Survey (OHAS) datasets. But wide distribution of these important and expensive data is limited by the requirement to protect the confidentiality of survey participants, who are guaranteed anonymity in exchange for participation. Data are often aggregated to a geographic level such as Census tracts or transportation analysis zones (TAZs) before disseminating to the public, which limits the utility of this information. This project aims to develop a methodology to permit dissemination of these spatially-explicit data to a wider range of public constituents while at the same time, protecting the identities of study participants. Making use of these data, this project will use geographical perturbation methods to add noise to the original data to protect confidentiality while at the same time allowing the detailed geo-spatial referencing to be included in the disseminated data. The process includes:

  1. The coordinates (locations) will be geographically perturbed (masked) so that they do not reveal the identity of the traveler while at the same time retaining spatial relevance and resolution. Here the limits of the perturbation (minimum and maximum displacement) need to be determined that a) ensure confidentiality and b) minimize the errors introduced to the data.
  2. The original and perturbed (masked) data will be compared using various statistical approaches to develop a set of confidence measures for different types of transportation applications. For example, geographically perturbed (masked) may be more robust for automobile travel measures, such as vehicle miles traveled (VMT) or transit accessibility than for detailed pedestrian or bicycle trip attributes.
  3. This exercise will allow spatially-explicit OHAS data to be released to the public with some information about the confidence to which it can be applied. These data will be archived at Portland State University and made available to the public.
  4. The information and algorithms can be shared with other agencies collecting and archiving travel data, such as the National Household Travel Survey, metropolitan planning organizations and state departments of transportation, to permit wider dissemination of their data.

Description

This is a final report, OTREC-RR-489, from the NITC program of TREC at Portland State University, and can be found online at: http://nitc.trec.pdx.edu/research/project/489.

The project brief can be viewed online at: http://archives.pdx.edu/ds/psu/17026.

DOI

10.15760/trec.116

Persistent Identifier

http://archives.pdx.edu/ds/psu/12106

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