Lisa Randall
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2 votes1 comment · U.S. DOT FY 2026 Phase I Pre-Solicitation Q&A » 26-OS2: Freight Corridor Predictive Intelligence · Admin →
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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 Lisa Randall
commented
The solicitation does not explicitly state a preference for this type of dual-functionality hardware. The FCPI project technical parameters emphasize the integration of existing data from sources like weigh-in-motion (WIM) systems and connected vehicle (CV) feeds, as well as the deployment of low-power EdgeAI devices at strategic freight bottlenecks to meet the core requirements (i.e., achieving a baseline corridor-level prediction accuracy of at least 80% for short-term (30–60 minute) congestion and delay forecasts). As such, the capability to monitor the structural integrity of a weigh station platform does not directly address the core purpose of the FCPI project. However, solutions that offer ancillary benefits may tend to score better on the selection criterion of “commercial potential” to the extent that the ancillary benefits make the solution more marketable.
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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 Lisa Randall
commented
USDOT expects the synthetic data generator to produce freight-domain-specific data (e.g., freight flows, corridor performance, system disruptions) rather than general-purpose tabular data augmentation. A core requirement for the Phase I proof-of-concept is to demonstrate a functional predictive AI architecture that integrates "combined real-world and synthetic freight datasets" for short-term congestion and delay forecasts.
Synthetic data must be robust and domain-specific enough to accurately represent those realistic trajectories and delay events tied to travel time reliability, congestion probability and truck delays. -
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 Lisa Randall
commented
A simulated federation is sufficient for a Phase I architecture demonstration; a live multi-party federated learning deployment involving distinct legal entities is not required until Phase II. For Phase I, the primary deliverable is to demonstrate a functional predictive AI architecture and build a preliminary model (proof of concept) for a selected freight corridor or segment using combined real-world and synthetic datasets. The actual implementation of federated capabilities to enable distributed model learning and real-time processing (while preserving data privacy) is identified as a Phase II outcome.
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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 Lisa Randall
commented
USDOT is not able to consider adding a new topic or expanding a current topic to the forthcoming FY26 Solicitation. Even though it cannot be considered for the FY26 solicitation, ideas can be submitted for the FY27 SBIR program through the "Suggest a Topic" on the USDOT SBIR website.
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4 votes1 comment · U.S. DOT FY 2026 Phase I Pre-Solicitation Q&A » 26-PH1: Increase End-of-Life Battery Safety and Recovery of Rare Earth Minerals · Admin →Lisa Randall
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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 Lisa Randall
commented
There is no strict priority order for data sources used for the FCPI project. The primary objective is to demonstrate a functional predictive AI architecture that successfully fuses a variety of multimodal public and private data sources to achieve a baseline prediction accuracy. Offerors should propose what they believe to be an optimal mix of datasets to support the intended use: “to enhance both public and private freight operational decision-making and to gain system-wide freight efficiencies.” For public data sources, the government anticipates provision of these for model training, calibration, and validation. For private-sector commercial data, offerors should not assume the government will automatically furnish private commercial data for Phase I efforts. One of the core Phase I deliverables is the Data Inventory and Integration Plan, which requires defining the specific access methods and availability for chosen data sources. Therefore, if an offeror’s solution explicitly relies on obtaining new, direct commercial licenses for proprietary private data, this should be included in the strategy and budget estimates.
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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 Lisa Randall
commented
A proposal utilizing a generative AI model trained on real-world data could align with the FCPI stated objectives. However, the solicitation is designed to avoid being overly prescriptive or showing a technology preference. An offeror should recommend a proposed approach they deem appropriate. Regarding the digital twin platform, there is no requirement to build a new system from scratch. The project emphasizes interoperability and compatibility with existing public and private-sector data and decision-support systems. Leveraging or building on top of an existing digital-twin platform is an acceptable approach, provided the solution meets the core requirement to eventually provide a cloud-hosted, web-accessible decision-support dashboard that is compatible with commonly enterprise platforms.
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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 Lisa Randall
commented
The Data Inventory and Integration Plan is primarily a strategy document, but offerors must still demonstrate functional data integration capabilities as part of Phase I work. The plan would involve identifying and assessing various public and private data sources and developing a data integration strategy that defines access methods, data availability, and preprocessing steps in preparation for Phase II implementation. The primary intent of Phase I is to test this exact data interoperability and predictive model feasibility. While you do not need fully operational ingest pipelines connected to live systems (as this is reserved for Phase II), Phase I work products should demonstrate how the proposed architecture can successfully ingest and fuse the multimodal data inputs that the offeror selects.
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2 votes1 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 Lisa Randall
commented
For the Phase I Commercial and Implementation Readiness Assessment, the core requirement is to evaluate commercialization pathways, partnerships, and integration opportunities with state DOTs, local DOTs, MPOs, and logistics data providers to support the transition to a deployable prototype. Because the focus is on assessing readiness and identifying opportunities, documenting early-stage interest that outlines a viable pathway to future collaboration is acceptable, but signed letters of intent are not required in this phase.
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5 votes1 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 Lisa Randall
commented
Physical edge hardware is not required for Phase I of the Freight Corridor Predictive Intelligence project. A high-fidelity software simulation environment or concept-level prototype is an acceptable primary validation platform for this initial phase. The primary intent of Phase I is to test data interoperability and predictive model feasibility.
The 80% baseline accuracy threshold for Phase I is intended to be evaluated against historical, archived datasets combined with synthetic data, rather than a live, real-time data feed. The primary objective of Phase I is to demonstrate technical feasibility and build a proof-of-concept. To achieve the 80% baseline accuracy requirement for short-term congestion and delay forecasts, offerors are expected to integrate and analyze combined real-world and synthetic freight datasets. Operational deployment and integration with live systems are explicitly reserved for Phase II.