Synopsis of New Methods and Technologies to Collect Origin-Destination (O-D) Data

Hard, Edwin; Chigoy, Byron; Songchitruksa, Praprut; Farnsworth, Stephen; Borchardt, Darrell W.; Green, Lisa · 2016 · ROSA P / United States. Federal Highway Administration

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This report, prepared by the Texas A&M Transportation Institute (TTI) for the Federal Highway Administration, addresses the evolving landscape of passive Origin-Destination (O-D) data collection. As traditional intercept surveys are replaced by automated technologies, agencies face uncertainty regarding the capabilities, limitations, and comparative performance of cellular, Global Positioning System (GPS), and Bluetooth data sources. The study aims to provide state-of-the-practice guidance to transportation agencies and Metropolitan Planning Organizations (MPOs) on selecting appropriate technologies for specific O-D study types, scales, and objectives. The methodology involves a comprehensive review of technical specifications, processing methods, and lessons learned from numerous studies conducted between 2010 and 2016. The report analyzes three primary technologies: cellular data (sourced from providers like Airsage), third-party GPS data (from vendors such as INRIX and HERE), and Bluetooth sensor data. The analysis compares these sources based on spatial and temporal resolution, sample penetration, accuracy, vehicle type differentiation, and data continuity. A key component of the evaluation is the 2014 Tyler, Texas, study, which directly compared O-D results from all three technologies within a single-county area to assess their relative performance for external-to-external (E-E) and external-to-internal (E-I) trips. The findings reveal distinct strengths and weaknesses for each technology. Cellular data offer high sample penetration and can distinguish between residents, visitors, and commuters, but suffer from coarse spatial accuracy (approximately 300 meters) and low temporal resolution (minimum three-hour increments). Consequently, cell data are best suited for aggregated zone-level analysis and long-distance corridor studies but struggle with urban network assignment. GPS data provide high positional accuracy (5–8 meters) and fine temporal resolution, making them suitable for detailed routing and commercial vehicle studies; however, they currently exhibit low sample penetration and a bias toward commercial vehicles. Bluetooth data rely on point sensors and are limited to detecting through-movements (E-E trips) rather than full trip ends, making them ideal for smaller-scale corridor studies and benchmarking other technologies but impractical for large-scale regional modeling. The report concludes that no single technology can comprehensively capture all O-D elements required for complex travel studies. While cellular data are cost-effective for broad population flows, GPS is superior for detailed routing and freight analysis, and Bluetooth serves as a reliable benchmark for through-traffic. The authors recommend that agencies consider combining technologies or purchasing specific data subsets from multiple sources to mitigate individual limitations. The report emphasizes that technology selection must be driven by specific study objectives, geographic scale, and required spatial resolution, noting that further research is needed to validate cell-based trip purposes and improve GPS sample penetration.

Key finding

No single technology can collect all elements needed for a comprehensive O-D travel study, making a combination of technologies the best approach for estimating all types and categories of O-D trips.

Methodology

review

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).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• exposure measurement

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: observational prevalence
• Methodological Resource: dataset resource