Traditional travel surveys have long been foundational to transportation planning. Household travel surveys, intercept surveys, and on-board counts provide detailed, self-reported insights into travel behavior. But as regions grow and travel patterns become more dynamic, surveys alone can no longer capture the full picture.
They remain valuable—but incomplete.
Even the most robust survey programs face inherent challenges:
Infrequency and long timelines: Surveys often take years to design, deploy, and analyze, making it difficult to reflect current conditions.
Small sample sizes: Results must be expanded to represent entire populations, introducing uncertainty.
Hard-to-reach populations: Visitors, seasonal travelers, and non-residents are difficult to capture through opt-in surveys.
Gaps in trip coverage: Non-commute, discretionary, and off-peak trips are frequently underrepresented.
For fast-growing or visitor-heavy regions, these gaps can materially affect planning decisions and model outcomes.
Big Data is not a replacement for traditional surveys. Its value lies in addressing what surveys struggle to measure at scale.
High-accuracy origin-destination (O-D) data derived from location-based services (LBS), such as that provided by AirSage, offers region-wide visibility into actual travel behavior. When used alongside survey data, it helps agencies:
Observe real-world travel patterns across all times of day
Capture populations surveys often miss
Reduce reliance on infrequent data collection cycles
Strengthen confidence in modeling inputs and results
Together, surveys and Big Data form a more complete and resilient data foundation.
The Maricopa Association of Governments (MAG), the MPO for the Phoenix metropolitan area, provides a clear example of how Big Data fills critical survey gaps.
MAG needed better insight into visitor travel—one of the most difficult populations to capture using traditional survey methods. By incorporating AirSage’s LBS-based mobility data, MAG was able to identify and analyze visitor travel patterns across the region without relying on costly, low-response surveys.
As Arup Dutta, Program Manager, Travel Demand Modeling Program at MAG, explains:
“With location-based services data we can identify some hard to opt-in populations such as the visitor population in this region. It's very difficult to do any kind of surveys for the visitor population and if there are some kind of surveys for this population, they have very limited data.”
This capability allows planners to move beyond assumptions and partial data toward a more accurate representation of regional travel.
AirSage’s origin-destination data is particularly well suited to complement traditional travel surveys because it provides:
High accuracy and reliability, rigorously vetted for planning and modeling applications
Significant time savings, eliminating the need to wait years for new survey cycles
Deeper insight, capturing visitor travel, non-commute trips, and temporal patterns surveys often miss
For travel demand modelers, this means faster access to defensible data that improves model calibration, validation, and overall confidence in results.
Traditional travel surveys remain an essential tool, but they are no longer sufficient on their own. When paired with high-quality Big Data, agencies gain a more comprehensive, current, and actionable understanding of how people move through a region.
Call to Action
Travel demand modelers looking to supplement survey data with origin-destination insights should contact AirSage or learn more about AirSage O-D Data/ Trip Matrices here: https://airsage.com/data-products/custom-trip-matrix/
