Admindotsbir@dot.gov
(SBIR/Volpe, Department of Transportation)
My feedback
78 results found
-
1 vote1 comment · U.S. DOT FY 2026 Phase I Pre-Solicitation Q&A » 26-OS2: Freight Corridor Predictive Intelligence · Admin →
An error occurred while saving the comment -
1 vote1 comment · U.S. DOT FY 2026 Phase I Pre-Solicitation Q&A » 26-OS2: Freight Corridor Predictive Intelligence · Admin →
An error occurred while saving the comment A limited digital twin focusing on synthetic disruption scenarios for a single corridor is acceptable and expected for Phase I; a broader, multi-corridor scope is not required at this stage. Scaling to a broader scope, such as conducting field pilots across 2–3 representative freight corridors with live data ingestion, will occur in Phase II. Defining how the offeror’s limited digital twin can accurately simulate and evaluate synthetic disruptions is a key component of Phase I.
-
1 vote1 comment · U.S. DOT FY 2026 Phase I Pre-Solicitation Q&A » 26-OS2: Freight Corridor Predictive Intelligence · Admin →
An error occurred while saving the comment Physical edge deployments are not required for Phase I; a simulated edge architecture or concept-level prototype is acceptable. The primary objective of Phase I is to establish technical feasibility and develop a preliminary model or simulation that validates the proposed predictive AI architecture and data-fusion approach. The offeror’s Phase I Conceptual Architecture and System Design should outline how the proposed solution system will integrate and scale to support the physical EdgeAI processing and distributed model learning capabilities that are anticipated for Phase II.
-
1 vote1 comment · U.S. DOT FY 2026 Phase I Pre-Solicitation Q&A » 26-OS2: Freight Corridor Predictive Intelligence · Admin →
An error occurred while saving the comment The FCPI project does not mandate a specific evaluation metric; rather, it is left to the offeror to define the metric that best fits their proposed solution. The solicitation does not prescribe a specific mathematical error calculation approach (such as MAPE, RMSE, or classification accuracy). Proposals should include recommended metrics and calculation approaches with supporting rationale.
-
1 vote1 comment · U.S. DOT FY 2026 Phase I Pre-Solicitation Q&A » 26-OS2: Freight Corridor Predictive Intelligence · Admin →
An error occurred while saving the comment Freight corridors include intermodal flows that involve interactions between modes (road, rail, port, air). The overarching objective of the project is to develop an AI-enabled predictive system capable of fusing "multimodal data sources" that can be applied to different types of freight corridors spanning “urban, rural and intermodal environments (e.g., road to rail to road, port to road, etc)
-
1 vote1 comment · U.S. DOT FY 2026 Phase I Pre-Solicitation Q&A » 26-OS2: Freight Corridor Predictive Intelligence · Admin →
An error occurred while saving the comment Phase I is primarily focused on demonstrating technical feasibility and a proof-of-concept; a fully operational, working corridor prototype is not expected until Phase II. Phase I efforts should prioritize proving that the proposed AI model and data-fusion approach work in principle, while providing a clear conceptual architecture and transition plan for scaling up to the fully working prototype expected in Phase II.
-
1 vote1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FR1: Non-Destructive Longitudinal Rail Stress Measurement Device · Admin →
An error occurred while saving the comment Yes, it will be considered. Communication details could help flesh out your idea.
-
1 vote1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FR1: Non-Destructive Longitudinal Rail Stress Measurement Device · Admin →
An error occurred while saving the comment the measurements of this device intended for private data collection (mostly industry, possibly FRA inspectors too, etc). At this time, there are no specific requirements for handling rail stress data collected by the devices securely and responsibly but these are worthy of consideration.
-
1 vote1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FT1: Exploring AI-based Predictive Maintenance for Public Transit Fleets · Admin →
An error occurred while saving the comment Yes, the topic mainly focuses on AI-based predictive maintenance for transit fleets (buses).
-
1 vote1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FT1: Exploring AI-based Predictive Maintenance for Public Transit Fleets · Admin →
An error occurred while saving the comment The topic doesn’t differentiate between transit agencies and their perspective operators and transit vehicles maintainers. As you mentioned, the main goal of this research is to explore the use of AI for transit vehicles maintenance at any level of transit vehicle owners, operators, and maintainers.
-
1 vote1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FT1: Exploring AI-based Predictive Maintenance for Public Transit Fleets · Admin →
An error occurred while saving the comment There is no restrictions on integrating IoT devices from multiple manufacturers to enable AI-driven predictive maintenance.
-
1 vote1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FT1: Exploring AI-based Predictive Maintenance for Public Transit Fleets · Admin →
An error occurred while saving the comment There is no restrictive on the use of data types and formats that can be used for the proposed solutions and as well as the research approaches the potential offeror wishes to explore. For the availability of data, government will not provide sample data. Potential offerors can reach out directly to any transit agency/ies for any data needed for their proposed solution.
-
2 votes1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FT1: Exploring AI-based Predictive Maintenance for Public Transit Fleets · Admin →
An error occurred while saving the comment The topic is open to AI-based predictive maintenance solutions to any public transit vehicles regardless of the energy sources of buses.
-
7 votes1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FT1: Exploring AI-based Predictive Maintenance for Public Transit Fleets · Admin →
An error occurred while saving the comment FTA doesn’t own and operate public transportation vehicles rather than providing financial assistance to transit agencies through formula and discretionary grants for the purchase of public transportation vehicles.
For the requested information, FTA's National Transit Database (NTD) could be a starting point. NTD was set up to be the repository of data about the financial, operating and asset conditions of American transit systems. Transit agencies report data on a number of key metrics including Vehicle Revenue Miles (VRM), Vehicle Revenue Hours (VRH), Passenger Miles Traveled (PMT), Unlinked Passenger Trips (UPT), and Operating Expenses (OE). However, this information might not be sufficient for the Potential offerors to conduct their research on this topic.
Potential offerors can reach out directly to any transit agency/ies if they need detailed data on the conditions of transit vehicle fleets.
-
4 votes1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FT1: Exploring AI-based Predictive Maintenance for Public Transit Fleets · Admin →
An error occurred while saving the comment Public transportation vehicles are owned and operated by transit agencies and the government has no direct involvement in any specific integration tools for transit vehicles management and maintenance.
-
2 votes1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FR1: Non-Destructive Longitudinal Rail Stress Measurement Device · Admin →
An error occurred while saving the comment Yes, using ultrasonics has been tried multiple times. The challenge has been to get a reliable measurement without knowing the “zero-stress-state” or RNT of the rail ahead of time for various types of rail and track constructions.
-
2 votes1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FR1: Non-Destructive Longitudinal Rail Stress Measurement Device · Admin →
An error occurred while saving the comment We expect the researchers proposing on this topic to be familiar with:
1) typical rail sizes (115, 136, 141, etc) used in the US transportation network
2) typical rail neutral temperatures (stress-free temperatures) (RNT) of the rails in the network (maybe 80 to 110F)
3) atypical RNT values after winter rail adjustments (maybe 20F or lower is reasonable)
4) strength of rail, coefficient of thermal expansion, and when a track might buckle
5) maximum rail temperatures (maybe 140 F or more)With the information above, one can calculate several estimates of rail stress. In addition, we would like to measure the stress accurately enough to determine the RNT of a given rail to +/- 5 F at a given time.
-
2 votes1 comment · U.S. DOT FY 2025 Phase I Pre-Solicitation Q&A » 25-FR1: Non-Destructive Longitudinal Rail Stress Measurement Device · Admin →
An error occurred while saving the comment You could work with a railroad or a rail manufacturer or plan a visit to the Transportation Technology Center in Pueblo, CO, where rails are available for testing. We don’t have a catalog of rails with specific amounts of residual stress, but we do have a couple of locations on track with known Rail Neutral Temperatures (RNT).
In this topic, please remember that we aim to measure longitudinal rail stress or RNT due to the rail being constrained on the track and changes in rail temperature. We are not interested in measuring residual stresses within the rail, although they can affect RNT measurements.
The overarching objective of the Freight Corridor Predictive Intelligence project is to investigate the blending of real-time edge analytics, generative AI, and federated learning to enhance both public and private freight operational decision-making. The solicitation explicitly seeks the delivery of proactive corridor management tools that serve state and local DOTs as well as private-sector freight operators.