Mobvynt fuses transit ridership, bike-share GPS and intersection camera feeds into a single demand surface — so planners see where the next route needs to go, not where the last survey said.
Transit ridership, bike-share usage, and intersection flows sit in separate systems with incompatible formats. Planners manually reconcile spreadsheets that are outdated before the analysis begins.
Origin-destination surveys capture a snapshot of travel behavior every 5–10 years. Post-pandemic ridership patterns have restructured fundamentally — the old data is no longer a reliable planning input.
Without a fused multimodal demand model, route redesign decisions rest on partial evidence. Agencies can't see where high-demand corridors are underserved or where service duplicates other modes.
Three integrated capabilities that move agencies from fragmented data to defensible planning decisions.
Normalizes and merges GTFS/GTFS-RT transit feeds, GBFS bike-share data, and intersection camera APIs into a single coherent dataset. No proprietary location tracking — only the open data your agency already produces.
Explore data sources →H3 hexagonal grid analysis at resolution 8 (avg 0.74 km² cells) revealing temporal demand patterns, mode split, and coverage gaps. More analytically precise than traditional Traffic Analysis Zone polygons.
Read the methodology →Route recommendation engine, coverage gap reports, modal shift analysis, and GTFS scenario export for CAD/AVL integration. Outputs designed for Title VI compliance documentation and FTA reporting.
View all outputs →Network redesign support, real-time demand monitoring, and Title VI equity analysis for service planning directors and transit planners.
Learn more →Intersection throughput analysis, signal timing inputs, and multimodal demand overlays for corridor optimization and incident impact modeling.
Learn more →Multi-jurisdiction demand surface, LRTP scenario comparison, CMAQ project prioritization inputs, and regional equity mapping for long-range planners.
Learn more →Mobvynt normalizes and fuses public transit and open datasets — no proprietary location tracking, no personally identifiable data.
The transit authority needed to understand whether pre-pandemic route structures matched post-2022 travel patterns. Mobvynt's demand surface identified corridors where ridership had structurally shifted.
The demand surface model gave us something we've never had before — a defensible, data-driven explanation of why we were reallocating service. That's what you need when you're going before a city council with a network redesign.
We'd been trying to make the case for a new BRT corridor for three years. The H3 analysis finally gave us the corridor demand data at the spatial resolution the LRTP required. The CMAQ application was approved on the first submission.
Book a 30-minute live demo. We'll connect to your GTFS feed and show you results from your own network.