AI for Transportation Planning and Design (AI TPD)

AI for Transportation Planning and Design (AI TPD) is sponsored by U.S. Department of Transportation (DOT), Intelligent Transportation Systems (ITS) Joint Program Office. This opportunity supports mission-aligned projects and measurable outcomes.

2. Artificial Intelligence 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 the Intelligent Transportation Systems Joint Program Office (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) 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 networks.

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 networks.