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Find similar grantsAI for Transportation Planning and Design (AI TPD) is sponsored by U.S. Department of Transportation (DOT), Intelligent Transportation Systems (ITS) Joint Program Office. The AI for Transportation Planning and Design (AI TPD) is an SBIR initiative to develop new AI-based decision-support software tools for transportation agencies.
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AI for Transportation Planning and Design | ITS Joint Program Office The AI for Transportation Planning and Design (AI TPD) initiative aims to bridge data silos in transportation agencies with advanced AI tools, enhancing safety, efficiency, and resilience through streamlined processes that reduce time and costs for actionable insights.
Fragmented, dated, and non-standardized data on surface transportation elements and hinder effective network analysis. Data collection and extraction is costly and difficult to implement at scale. Existing tools lack capabilities to assist with decision-making about asset siting, design, and deployment.
AI for Transportation Planning and Design (Al TPD) is an SBIR initiative funded by ITS JPO to develop powerful new Al-based decision-support software tools that generates data at HD resolution and assists in the siting, design, and deployment of infrastructure.
Example: Identify street segments with high rates of near-misses, or a traffic event that produces more than an ordinary amount of danger to the drivers and passengers involved and propose infrastructure and operational changes to avoid future modal conflicts.
Spotlight on Phase 1 Awardees Complete Pavement Markings for Safe and Complete Streets Artificial Intelligence Processing and Analysis Framework for Infrastructure Planning, Design, and Maintenance Scalable and Sustainable AI Solutions for Complete Streets Data Creation and Maintenance with Off-the-shelf Dash Cams Informing infrastructure interventions via novel near-miss data collection Artificial Intelligence (AI) Approach to Generate and Analyze Transportation Planning & Design (TPD) Data at Scale Safely Advancing Multimodal Mobility Intelligence (SAMMI) Urbanomy: AI-Driven, Multi-Modal Decision Support for Smarter, Scalable Transportation Design, Planning, and Asset Management Skylite Geo LLM Transportation Planning and Decision Support Platform Complete Urban to Rural Balanced Streets by Artificial Intelligent Design (CURBS-AID) Safe Routes for All (SR4A) AI for Comprehensive, Efficient, & Safe Streets (ACES) SBIR Fiscal Year 2024.
2 Phase II Awards: AI for Transportation Planning and Design (AI TPD) Volpe Center | Small Business Innovation Research SBIR Fiscal Year 2024. 2 Phase II Awardee Abstracts: AI for Transportation Planning and Design (AI TPD) Volpe Center | Small Business Innovation Research Meet the 12 Phase I Awardees SBIR Fiscal Year 2024.
2 Phase I Awards: AI for Transportation Planning and Design State, regional, local, and tribal transportation agencies are under increasing pressure to plan, design, and deliver projects that keep pace with rapid changes in population growth, freight movement, and infrastructure needs.
Today's transportation data, ranging from traffic counts and freight flows to safety statistics and crash locations, is vast, complex, and often siloed across different systems, companies, and third-party resellers. Integrating these diverse datasets into clear, actionable insights remains a significant challenge, slowing planning processes and creating inefficiencies in project delivery.
The AI for Transportation Planning and Design (AI TPD) initiative is a $15 million, multi-phase federal effort to address these gaps by bringing the power of artificial intelligence directly to transportation agencies. The initiative equips agencies with advanced AI tools to identify safety risks, detect network gaps, and automate aspects of planning and design.
These capabilities allow agencies to simulate and compare design strategies, assess performance across modes and corridors, and improve safety outcomes system-wide.
By streamlining data analysis and design evaluation, the initiative reduces the time and cost required to translate information into action, thus supporting safer, more efficient, and more resilient transportation This effort is funded through the U.S. DOT Small Business Innovation Research (SBIR) program, which provides seed funding to promote early-stage research and development.
Phase II and IIB outcomes rely on the specifications established in the initial solicitation. While the SBIR program is more structured than other funding mechanisms, it offers small businesses an opportunity to develop innovative methods that advance the state of practice. The phased approach allows promising ideas to mature, scale, and transition toward commercialization.
AI-Enhanced Data Collection and Integration Transportation agencies often face persistent data challenges: information is fragmented across jurisdictions, inconsistent in quality, and incomplete across modes. Reliable, real-time data on emerging systems, such as connected and automated vehicles (CAV), micromobility, and active transportation, is especially limited.
These data gaps make it difficult to anticipate system needs, identify safety risks, or design coordinated, multimodal networks. The AI for Transportation Planning and Design (AI for TPD) initiative is exploring how artificial intelligence can transform how transportation data is gathered, cleaned, and integrated.
Using computer vision, machine learning, and Internet of Things (IoT) technologies, the program supports new methods to automatically extract information from diverse sources such as satellite and street-level imagery, LiDAR, sensor feeds, dashcam video, crowdsourced inputs, and vehicle probe data.
AI algorithms can classify roadway elements, detect changes over time, and estimate travel behaviors at scales and speeds that are not feasible through manual analysis. These approaches improve data coverage, timeliness, and consistency, creating a stronger foundation for safety analysis, performance monitoring, and automated decision support.
By building high-quality, continuously updated datasets, agencies can more effectively plan, evaluate, and maintain safer and more resilient transportation systems. AI-Enhanced Decision Support Tools Building on improved data integration, the AI for TPD initiative is advancing new tools that use artificial intelligence and large language models (LLMs) to make transportation data easier to explore and apply.
These tools will allow users to query complex datasets in natural language, asking questions such as "Where are the highest-risk pedestrian corridors?" or "How might proposed designs affect freight travel times?" and receive clear, visual responses.
By combining AI analytics, geospatial visualization, and dynamic simulation, agencies can test scenarios, compare design options, and monitor performance across safety, mobility, and efficiency metrics.
This reduces the need for manual data processing and specialized modeling, enabling faster, more transparent, and evidence-based decision-making across transportation This list of datasets and resources can help teams understand what data is already available and where there are gaps, as well as what resources can help them to learn more about While the primary audience for the AI TPD decision support tools are public sector transportation agencies, other stakeholders such as academia, non-profits, the general public and the private sector may find also find value in their use.
All questions on the pre-solicitation and solicitation should be directed to the DOT SBIR Program at dotsbir@dot. gov . For other questions, you can reach the AI TPD team at aitpd@dot.
gov .
Based on current listing details, eligibility includes: Small businesses eligible for the U. S. DOT SBIR Program. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates $15 million initiative (individual award amounts not specified but SBIR Phase I can range from $50,000 to $275,000, Phase II $750,000 to $1.8 million) Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
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This NOFO provides an opportunity to all FY 2018 NIST SBIR Phase I awardees to submit a Phase II application following completion of Phase I. This NOFO provides instructions for FY 2019 NIST SBIR Phase II application preparation and submission requirements. In Phase II, work from Phase I that exhibits potential for commercial application is further developed. Phase II is the R&D or prototype development phase. To apply for a Phase II award, each Phase I awardee will be required to submit a comprehensive application outlining the proposed research and a detailed plan to commercialize the final product. Each NIST Phase II award is for up to $400,000 and up to a 24-month period of performance. One year after completing the Phase II R&D activity, the awardee shall be required to report on its commercialization activities. Up to an additional $6,500 may be requested for Technical and Business Assistance (TABA); see Section 5.11 for more information about TABA. Funding Opportunity Number: 2019-NIST-SBIR-02. Assistance Listing: 11.620. Funding Instrument: CA. Category: ST. Award Amount: Up to $400K per award.
Local Government Cybersecurity Grant Program (Florida) is sponsored by Florida Digital Service. This Florida state grant program enhances cybersecurity resilience in local governments, with a priority focus on fiscally constrained rural areas. Rather than issuing direct funding, the Florida Digital Service will procure cybersecurity solutions directly on behalf of awarded applicants. The grant supports new or expanded capabilities in preventing, detecting, responding to, and recovering from cyber threats.
NVIDIA Graduate Fellowship Program is a grant from NVIDIA providing up to $60,000 per award to PhD students conducting research that advances accelerated computing and its applications. Now in its 25th year, the program invites nominations from doctoral students pushing the boundaries of artificial intelligence, robotics, autonomous vehicles, and related fields. Recipients receive not only research funding but also access to NVIDIA technology, products, and engineering expertise, along with a mandatory in-person summer internship. Students are nominated by their faculty advisors and selected based on academic achievement and research area alignment.