Numina delivers real-time mobility insights from streets to make cities more responsive — namely, by building a street-level data utility to support walkability, bikeability, interactivity, and efficiency in cities. Our standalone sensor platform uses computer vision to measure how people and objects move throughout streets and public spaces. Numina’s real-time intelligence helps planners and municipal governments understand, map, and improve how streets and public spaces are actually used. Numina provides counts of each type of traveler or object, as well as their speeds, paths, directionality, proximities to one another, dwell times in key locations, and more — all of which accessible through a custom data dashboard and a real-time API.
The public and private sectors invest billions of dollars in infrastructure every year, informed by old, static census data and by car counts. Meanwhile, cities lack data about how people move — whether on foot or bicycle or other modes. Cities therefore can easily justify infrastructure for cars but not necessarily for people or for multimodal trips. As transportation trends shift in ways we’ve never seen before, there’s a new opportunity for Pittsburgh to make targeted data-driven decisions regarding urban mobility and multimodal travel. Numina measures all modes in streets so that cities have such data, to inform more equitable and more sustainable infrastructure, which will support a more seamless and enjoyable transportation experience for everyone.
Numina is an end-to-end solution that delivers real-time street-level intelligence, with a focus on the things that don’t move in lanes and are missed by traditional traffic sensors. Numina generates the “desire lines” of pedestrians, cyclists, wheelchairs, scooters, trucks — all kinds of things that planners and mobility operators need to better understand in order to design safer and more enjoyable transportation experiences.
Our hardware—made in Long Island City, Queens, NY—very easily mounts to street infrastructure (such as light poles) and detects pedestrians, bicycles, different classes of vehicles (cars, buses, trucks), and other objects in streets, plazas, and public spaces. The insights that Numina produces off of this anonymous, aggregate data include counts of each type of traveler or object, as well as their speeds, paths, directionality, proximities to one another, dwell times in key locations, and such events as near misses.
It is our commitment to provide intelligence without surveillance. By design, the sensor processes all raw data onboard the device, only distilling it down to the smallest, critical, anonymous data necessary. To glean meaningful insights, the system does not store or transmit off-site any imagery or personally identifiable information.
In addition to providing our data via an intuitive web dashboard, Numina’s platform also publishes data via JSON API, which updates by the minute and which can be easily integrated into any number of applications, data analysis, or processing tools outside of our proprietary technology stack. As Pittsburgh has been such a leader in new mobility technology, it is an ideal testbed for Numina data to, for example, feed multimodal data into autonomous vehicle training simulations, so they can train on real-world behaviors rather than on idealized (i.e. unrealistic) assumptions about human behavior.
HOW WE RISE TO THIS CHALLENGE
By building and providing this data utility for Pittsburgh, we will enable its planners, designers, and engineers to make targeted decisions around the design, programming, and infrastructure for mobility in the city or—in other words—smarter transportation choices. Simultaneously, because this data is anonymous, aggregated, and secure, it is an asset that local stakeholders can share in near-real time for all kinds of other mobility and community applications.
This solution will also make the City’s transportation system adaptable to commuter needs that may change throughout a day, week, or month — subsequently enabling the introduction of multimodal and flexible transportation options. Our continuous data streams enable our users to see how public spaces and streetscapes are actually used, inclusive of all kinds of travellers and accurate across modes. We enable planners and transportation engineers to adjust transit schedules according to traffic flow; designers can model the impact of various design interventions on safety and curb utilization; and rideshare, bikeshare, and electric vehicle companies can manage the distribution of fleets based on the City’s daily (or even minute-by-minute) needs— among many other flexible use cases. In the flexibility of our sensors and data, Numina’s solution will aid the City in its responsiveness to the residents’ changing transportation needs; in the accuracy of our data across modes, Numina will aid the City in increasing the viability of the existing transportation network for non-motorists.
The real-time nature of Numina data also makes it possible for the system to trigger city services where needed, when needed. Numina’s real-time data helps maintenance and public safety departments identify anomalies (e.g. obstructions like trash piles or potholes, or sudden behaviors like crowds running in one direction) and automatically deploy maintenance or emergency services, or automatically provision traffic enforcement services to address idling rideshare vehicles at the curbside or in loading zones and create new revenue for municipalities.
Since Numina never collects any personally identifiable information, the data is not intended for enforcement against individuals. It is meant to help us shape cities to humans’ real-world behavior and desires — which can help us strengthen and amplify public- and private-sector efforts in economic development, social science, public health, and more. Such a data infrastructure promotes the awareness and coverage of mobility options across the city, empowering mobility providers to create stellar travel experiences and residents to utilize a broader menu of transportation modes beyond single-occupancy vehicles.