 |  |  |  |
Adding Value to GPS Travel Data with New Open-Source Processing Software for Everyone
Principal Investigator:
Jennifer Dill, Portland State University
Summary:
Researchers and practitioners have a growing interest in using Global Positioning System (GPS) based travel data to augment or even to replace traditional diary-based surveys. GPS data promise improved accuracy and more detailed spatial data. Largely passive collection can reduce respondent burden considerably, permitting longer survey periods. The advantages should be greatest for non-motorized t...
Researchers and practitioners have a growing interest in using Global Positioning System (GPS) based travel data to augment or even to replace traditional diary-based surveys. GPS data promise improved accuracy and more detailed spatial data. Largely passive collection can reduce respondent burden considerably, permitting longer survey periods. The advantages should be greatest for non-motorized travel where high spatial resolution, capture of easily forgotten short trips, and longer collection periods can provide a clearer picture of pedestrian and cyclist patterns, needs, and preferences.
The enticing promise of GPS travel data has been tempered by the challenges of processing and analyzing the raw data once collected. Unfortunately, existing GPS processing techniques are not easily accessible to agencies or other researchers that have or would like to collect GPS travel data. In addition, many processing techniques have been adapted from motor vehicle-based GPS, and it is unclear how well these methods transfer to non-motorized travel. The proposed project would translate existing research into an open-source software tool that an agency or researcher could use to process their own GPS travel data in a standardized, repeatable way. A real-world test dataset from Portland, OR would provide a better idea of the expected accuracy of existing methods for multi-modal data.
See More
Project Details
| Project Type: | Technology Transfer |
|---|---|
| Project Status: | Completed |
| End Date: | December 31, 2016 |
| UTC Funding: | $47,049 |