Downtown.AI Leverages Machine Learning and Big Location Data to Analyze and Predict Human Movements in Cities
We are disrupting the way Mobility Services and Transportation Authorities around the world forecast customer demand and optimize traffic.
Our cloud platform uses proprietary machine learning algorithms, large scale mobile location data and other sources to analyze, map and forecast the movement of entire cities' populations - including pedestrian traffic, cars, public transportation and micro mobility commutes.
With real world data and ML computing power we turn traffic analytics into a science.
Human Movement Predictive Analytics
We help mobility services to predict customer demand and optimize traffic in cities
Downtown.AI human movement SaaS platform enables users to explore the data and generate ready-to-use analyses, forecasts and dynamic human movement maps.
The platform generates insights on past, current and future traffic in a given geography. Features include the ability to analyze specific modes of transportation, demographic segments, Zip Codes, commuter behaviours and more. Users of the platform can extract powerful insights into the flow, volume and traffic speeds in specific streets, cities or an entire country.
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Public Transit to and from Union Station, Toronto Ontario. An hour by hour analysis. 6 am till midnight. In green - traffic to the station. In red - traffic from the station. Tipping point around 3PM.
Downtown.AI's platform enables Smart Mobility Services (Carsharing, Ridesharing, Micro Mobility) to accurately forecast customer demand, to compare and pick the best cities to expand to, and to optimize placement of vehicles and assets.
We help Transportation Authorities to improve planning of new projects (lines, stations, intersections...), to optimize scheduling of buses and trains, to evaluate the impact of past investments and to manage daily traffic.
We assist Car OEMs to enable Autonomous Cars to identify crowds' hot spots (e.g. for Ridesharing services) and to improve navigation in cities crowded by pedestrians.