Sense-making of imperfect, massive geospatial datasets is foundational to everything else we do. We’re experts in efficiently determining staypoints, interpreting driver behavior, and even automatically detecting which intersections in a road network use stoplights.

The real power of Mobiliti is in being able to simulate emergent behavior when that equilibrium is disrupted. To do this effectively at the scale of billions of events per day, we’ve taken an innovative approach to parallel discrete event simulation – an algorithm through which the impact of sequential events on a system can be processed in parallel without losing the causality between those events.

The future of transportation management lies not only in understanding the state of the world, but also in proactively managing it. To this end, we’ve developed neural networks to accurately predict future highway traffic, and are investing in reinforcement learning approaches to manage dynamic traffic signal timing.

The GPS-based services we rely on today (Google Maps, Waze, Uber, Lyft) all optimize for minimizing vehicle travel time. But that approach comes with externalities – a famous example being quiet residential streets suddenly facing a deluge of Lyfts taking a shortcut. Our Mobiliti analytics allow us to explore the impacts of alternative routing strategies – on safety, on fuel consumption, and much more.