Mobility today means more than simply moving vehicles. Planners must now design systems that efficiently move people across diverse, overlapping modes — walking, biking, transit, rideshare, and even autonomous vehicles. To plan and evaluate these complex networks, transportation professionals need visibility into how people actually move through space and time. That’s where location-based services (LBS) data plays a pivotal role.
LBS data — derived from anonymized location signals collected through mobile devices — provides a dynamic, real-world view of human movement. It delivers several major advantages for multi-modal planning:
Unlike traditional sensors or ticketing data that capture only one mode, LBS data can present entire trip chains — walking to a bus stop, taking transit, then using a shared scooter. This multi-modal continuity helps planners understand complete journeys, not isolated trip legs.
Because it captures travel activities 24/7, LBS data supports analyses of peak-hour congestion, off-peak transit use, and weekend recreation trips — all without costly long-term surveys.
Modern datasets can reveal trip patterns down to fine spatial grids, uncovering network gaps, pedestrian exposure risks, and first/last-mile challenges that traditional datasets and observations often miss.
Unlike household travel surveys that can take years to complete, LBS data can deliver results in days or weeks — supporting rapid response to shifting travel patterns, such as post-pandemic and “return to office” mode shifts or seasonal demand spikes. Historical data is also available to provide insight on conditions at a prior point in time.
No single dataset provides a full picture of travel behavior. LBS data works best when integrated with other mobility datasets to enhance accuracy and context.
| Data Source | Strengths | Limitations | Best Use Cases |
|---|---|---|---|
| LBS / Mobile Device Data | Broad coverage, cross-modal visibility | Potential sampling bias; mode inference challenges | OD estimation, trip purpose inference, access analysis |
| Connected Vehicle (CV) Data | High positional accuracy; vehicle-level detail | Vehicle-only perspective | Traffic flow, safety analysis, speed validation |
| Transit Smartcard & AVL Data | Network-specific precision | Mode-limited; no first/last-mile info | Transit reliability, stop-level ridership |
| Household Travel Surveys | Rich demographic context | Small samples, high cost, labor intensive | Behavioral modeling, calibration, validation |
| Bluetooth / Wi-Fi Sensors | Accurate local tracking | Limited geographic scope | Corridor monitoring, event impact |
Used together, these sources can produce the most accurate and policy-relevant mobility insights.
While powerful, LBS data must be handled with care:
Representation: Not all populations are equally captured due to device ownership and app sampling. Weighting against census data can improve representativeness.
Mode Identification: Inferring mode (e.g., bike vs. car vs. bus) requires modeling and cross-referencing with contextual data.
Privacy: Responsible data providers apply aggregation and anonymization protocols to ensure individuals cannot be identified.
Coverage Gaps: In rural or cross-border areas, data density may drop — requiring smoothing or integration with other sources.
As cities pursue climate goals, equity commitments, and infrastructure investments under programs like IIJA and SS4A, the need for comprehensive mobility intelligence has never been greater.
LBS data empowers planners to:
Quantify actual travel demand across all modes
Identify first/last-mile barriers
Evaluate shared mobility and transit access
Monitor behavioral shifts in near real time
This multi-modal perspective helps agencies design systems that truly serve people — not just vehicles.
The future of transportation planning is data-driven, integrated, and human-centered.
AirSage is a pioneer in location-based mobility intelligence, transforming billions of anonymous location signals into actionable insights. With its customizable Origin-Destination (O-D) Trip Matrix, AirSage helps transportation agencies, consultants, and researchers understand how people move across regions and modes by time of day — supporting smarter, more sustainable planning decisions.
Interested in seeing what LBS data can reveal about your transportation network?
Explore AirSage’s O-D Data Solutions →
