2025

Road Weather Information Systems (RWIS) are widely relied on for agency operations and need to be accurate and reliable to the end of their lifecycle given maintenance and funding limitations. ENTERPRISE members are interested in understanding how RWIS solutions (specifically to support ITS and operations) available from different vendors differ in terms of accuracy, reliability, and costs. This effort seeks to understand the accuracy, reliability, and cost tradeoffs of deployed RWIS solutions by documenting available RWIS solutions, reviewing available documentation of RWIS asset management, and surveying state DOT practitioners, with an emphasis on RWIS supporting ITS solutions. 

More demand for truck parking than available capacity is a challenge facing state transportation agencies. When this occurs, trucks may choose to park on roadway shoulders. Many states have deployed systems to automatically monitor/detect truck parking availability and communicate this information to truckers as they are approaching truck parking facilities. This is typically accomplished through in/out systems that monitor/detect vehicles as they enter and leave truck parking lots or by space-by-space systems that monitor/detect individual truck parking spaces. ENTERPRISE members have had some experience with truck parking detection and expressed interest in better understanding these technologies, components, and dissemination mechanisms. Other key interests were innovative truck parking detection concepts and methods, safety concerns and roadway maintenance issues with trucks parking illegally or on the shoulders at rest areas, maintenance efforts for truck parking detection systems, and other industries that utilize vehicle detection technology. Documenting examples of these truck parking interests was accomplished through outreach with ENTERPRISE member states and a literature review.

A number of intelligent transportation systems (ITS) products and approaches have used “machine learning” for decades. Building on this foundation, recent artificial intelligence (AI) products and increased availability of AI have increased use cases for AI in transportation.

This project was conducted to introduce ENTERPRISE members to AI and facilitate understanding about how AI is being (and may be) applied to transportation, as well as the potential uses, benefits, and challenges of AI. Specifically, this project:

• Documents example use cases of AI in transportation operations. 
• Provides a definition and high-level context of AI compared to other solutions;
• Documents and synthesizes available national materials;
• Summarizes state-level AI policies to document themes and additional considerations;
• Identifies considerations for transportation agency practitioners interested in using AI in operations; and
• Documents example use cases of AI in transportation operations.