Using GPS for Measuring Household Travel in Private Vehicles
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Summary
This paper addresses the limitations of traditional self-reported household travel surveys, which rely on written diaries and telephone retrieval. These conventional methods suffer from poor data quality regarding trip start/end times, destination locations, and short-trip reporting, while imposing a significant burden on respondents, often requiring over 20 minutes per person for a single day’s recall. To mitigate these issues, the authors developed a proof-of-concept system combining Global Positioning System (GPS) technology with Personal Digital Assistants (PDAs) to automatically capture vehicle-based travel data, aiming to improve data accuracy and reduce respondent effort. The study employed a field test conducted in Lexington, Kentucky, from September to December 1996, involving 100 households. The data collection unit consisted of a Sony MagicLink 2000 PDA connected to a Garmin GPS receiver, powered via the vehicle’s cigarette lighter. Drivers used a touch-screen interface to input trip purpose and occupancy, while the device automatically recorded date, time, and position at frequent intervals. After a six-day usage period, units were mailed back for processing using Geographic Information Systems (GIS) to calculate travel speed, distance, and route classification. The study also included post-usage telephone interviews to assess ease of use, privacy concerns, and to collect recall data for comparison with machine-recorded metrics. Results indicated high user acceptance, with over 70% of respondents rating the device as “very easy” to use and nearly all preferring it over written logs. Data analysis revealed that machine-recorded trip start times were evenly distributed, eliminating the rounding errors common in self-reported data, which typically showed peaks at quarter-hour intervals. The Lexington sample averaged 4.7 trips per day with an average trip length of 6 miles. While the technology successfully captured precise route choices, travel speeds, and functional road classifications—data previously difficult to obtain via telephone surveys—it did not fully eliminate unreported trips, as the device required manual activation. Privacy concerns were minimal, though some respondents expressed anxiety about vehicle theft. The study concludes that integrating GPS with handheld computers is a functional and effective method for household travel surveys. This approach significantly enhances data quality by providing objective measures of time, distance, and route choice, while reducing the cognitive and temporal burden on participants. Although GPS signals may be obscured in urban canyons or dense tree cover, the technology proved sufficient for plotting most trips without differential correction. The authors suggest that future iterations could automate device activation to further reduce missing data, positioning this hybrid method as a superior alternative to traditional diary-based surveys for transportation planning and policy analysis.
Key finding
GPS-recorded trip start times were evenly distributed across hours, whereas self-reported times exhibited significant rounding peaks at quarter-hour and five-minute intervals.
Methodology
field_study
Sample size: 100
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Empirical Findings: observational prevalence, self report data
- Methodological Resource: dataset resource