State & Local Innovation

INRIX x MetroLab

01.08.26 | 6 min read

We’re seeking innovative research and local government collaborators from the MetroLab Network looking to support positive human-centered outcomes in cities. We’re selecting up to 5 teams to join this public-private partnership that will provide free access to a range of INRIX APIs for up to 1 year.

Applications will be judged by INRIX, FAS MetroLab, and an external third party transportation and mobility expert.

Applications close March 3, 2026.

Requirements

1. Confirm that your institution is a current Friend of MetroLab. If you are not a Friend of MetroLab, click here to sign up — this is free.
2. Submit your $250 application fee. This fee is waived for individuals affiliated with Historically Black Colleges and Universities (HBCU) and/or Minority Serving Institutions (MSI). 
3. Submit your application(s) by March 3, 2026. Multiple teams from the same institution are eligible to apply. 

Challenge Details

Available INRIX Datasets:

• Trips Origins and Destinations + Trip Paths. Trip Analytics is derived from industry-leading geospatial data processing, which enables a new understanding of population movement information such as origin and destination zones, trip paths taken, diversion routes during peak time and incidents, corridor usage and more.
• Speed/Real Time Traffic API. Includes real-time traffic flow, incident/congestion alerts, bottlenecks, and camera views (where available) ― provides agencies with a complete, real-time picture of current traffic conditions.
• Traffic Tiles. Tile-based overlays for INRIX Fusion Traffic Flow that include images with lines representing road segments which are coded to the traffic conditions on that road segment.
• Incidents. Incidents generated from multiple incident sources including: manned operations centers, consumer reports, DOT cameras, social media and other observational tools. Based on TMC coverage, each incident includes the segment location, a roadway description, a journalistic description of the incident, start and end point of the incident and the last detour point plus average speeds along the congested segment. Incident types include INRIX Flow Incidents and accident, event and construction incidents. Incidents are available in TPEG-TEC, verbose XML or Alert-C compliant formats.
• Dangerous Slow-Downs. This service rapidly and reliably monitors the location, duration and length of queues at a pixel level, for detecting impact on traffic flow operations. Generated from INRIX Fusion speed data, this active safety application informs users of the presence of potentially hazardous slowdowns by rapidly and reliably monitoring the location, duration and length at a sub-segment level. Enabling drivers to better anticipate emergency braking situations and road operators to deploy appropriate accident prevention strategies.
• Volumes. INRIX Volume Profiles is an industry leading historical vehicle count product that uniquely provides day-parted and direction-parted vehicle counts for the entire United States. For each 15 minutes of each day of the week, totaling 672 time bins.
• Speed Distribution Profiles. Represents the statistical distribution of speeds. The distribution is built by taking the observed vehicles over a segment for the timespan of the request. The data is compiled into 15 min daily bins which can be rolled up to 1-hr, 1-day, or specific span of time (commute hours, mornings, evenings, etc.) as the use case requires.
• DT Polygons. DriveTime Polygons leverage real-time traffic conditions to visually display dynamic travel times as a range using a central point of origin as a reference point. Accessed via API, these drive time ranges are ideal as a layer on top of a map.
• Trips Plus. Trips Plus combines INRIX’s connected vehicle data (passenger cars and trucks) with U.S. POI data that is comprised of over 12mm POIs, 450 tickers, and 7,400 brands (a brand is any company that has three or more locations). With Trips Plus, users gain insights into consumer trends to locations such as theme parks, casinos, restaurants, hotels, retail, etc. Users can also analyze fleet/truck activity around places such as manufacturing facilities, warehouses, distribution centers and seaports.
• Off-Street Parking. INRIX Off-Street Parking provides static and dynamic parking data for off street parking facilities. Data provided includes parking lot locations, entrances and exits, hourly and daily rates, hours of operation, payment type accepted, structure type (such as roofed, underground, open, etc.), and special access capabilities. Dynamic availability predictions are also included as well as some real time occupancy feeds.
• On-Street Parking. INRIX On-Street Parking provides parking high resolution curbside data for on-street parking locations which includes pricing, restrictions, and real time availability prediction. In dense urban areas in 125 cities, INRIX provides 100% geographic and temporal coverage of curbspace at 1 meter resolution.

Project Wishlist

We are open to any creative ideas that your team has but to spark some creative juices, here are some ideas we’ve been thinking about:

• Identifying the changes to traffic patterns pre and post pandemic for urban, suburban, and exurban areas and the economic impact to cities and transit agencies because of these changes
• How do different on-street and off-street parking regulations as well as transit, micromobility, or other services and subsidies impact parking availability and total vehicle miles traveled based on cars cruising for availability
• Measuring passenger and freight trips mixed with open source or publicly available shared mobility and transit data to identify the impact of different policies, subsidies, and infrastructure improvements
• Linking trips, parking, and external sources around active transportation trips and financial information such as credit card data or tax receipts to understand the financial impact of different types of operational, infrastructure, and parking changes
• Does monitoring truck activity around manufacturing facilities, such as John Deere, Caterpillar, Procter & Gamble, etc, provide a leading indicator to future financial gains for that respective company? Does monitoring passenger car activity at these same facilities provide additional context since one can infer that these are employees that work there (ie. is employee activity increasing/decreasing)?

Previous Winners

2025 Finalists

• Columbia University. Validating and enhancing AI-driven traffic models for New York City using INRIX data and fusion of over 500 traffic cameras.
• University of California, Berkeley. Analyzing the true impact of automated speed cameras on driver behavior and safety ahead of a new state-wide pilot program.
• University of New Mexico. Identifying the hidden environmental factors of crash hotspots to better protect cyclists, pedestrians, and other vulnerable road users.
• Utah State University. Using mobility data to understand vehicle abandonment during wildfire evacuations, providing critical information for emergency response.
• New York University. Quantifying driver adaptation and behavioral shifts under NYC’s congestion pricing program using INRIX Trips Origins and Destinations data.
• Florida A&M University and Florida State University. Improving disaster evacuation planning by using INRIX trip data to predict sudden traffic surges and adjust transit services in real-time.
• University of Alabama. Analyzing INRIX trip and speed data to understand how tornado warnings change driving behavior and traffic flow in Tuscaloosa to aid emergency planning.
• University of Alabama. Designing a patient-centered transportation service for rural areas by using INRIX speed and trip data to simulate fleet operations and improve healthcare access.
• California State University, Fresno. Pinpointing California’s most dangerous roads by combining INRIX traffic volume data with fatal crash records to quantify risk disparities between rural and urban areas.

2024 finalists

• University of Washington and Argonne National Laboratory. Leveraging the Argonne Polaris Model, the University of Washington used INRIX data to calibrate a model of the Puget Sound region that would help enable system-level exploration of decarbonization strategies. The University of Washington used INRIX’s Trips and Speeds data to calibrate spatial and temporal trip level details as well as system level travel times which helped deliver key insights related to real world travel patterns across the Seattle area. From these existing conditions, the model then simulates the impacts of decarbonization strategies like EV charging, demand management, or land use changes. This reproducible workflow can be applied to regions throughout the country to provide more accurate real world existing conditions and greater benefits for decarbonization strategies. Argonne and its partners are currently exploring replicating this strategy in the Chicago and Austin metropolitan areas.
• University of Notre Dame and City of South Bend. In partnership with the City of South Bend, the University of Notre Dame studied the impact of physical traffic control measures like raised crosswalks and intersections and speed limit changes on local streets that link major arterials. They analyzed two parallel streets – one with 1 raised crosswalk and 2 raised intersections versus another street with only posted speed limit signs. In both instances, the speed limit was changed from 35 mph to 25 mph. The team leveraged INRIX’s speed distribution profile and volumes data to measure the potential exposure for vulnerable road users of cut through traffic. What they found was the biggest impact to lower speeds was where there was a physical traffic control measure paired with the lower speed limits whereas the speed limit change had little impact to observed speeds. The team is now producing a microsimulation model to further scale their program to analyze and implement safety improvements.