Implementing Machine Learning at State Departments of Transportation: A Guide

By: National Academies of Sciences, Engineering, and Medicine and Transportation Research Board and National Cooperative Highway Research Program and Kaan Ozbay and Kelly Bare and Sherif Ishak and Mecit Cetin and Matthew Samach and Haley Townsend

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Synopsis: Within the last two decades, Machine Learning (ML), the main subfield of Artificial Intelligence (AI), has gained significant momentum across all sectors, driven by a confluence of factors: exponential growth in data generation, advancements in data storage and computing, and innovations in algorithmic techniques. Most notably and recently, the proliferation of deep learning (DL) methods and generative AI tools (GATs) such as ChatGPT are revolutionizing the business landscape. In an era where data is pouring in from new sources, the pace of data growth is exceeding the pace at which state and local Departments of Transportation (DOTs) are able to use it. NCHRP Research Report 1122: Implementing Machine Learning at State Departments of Transportation: A Guide, from TRB's National Cooperative Highway Research Program, serves as both an education and a decision-making tool to assist state DOTs and other transportation agencies in identifying promising ML applications; assessing costs, benefits, risks, and limitations of different approaches; and building a data-driven organization conducive to capitalizing on and expanding ML capabilities in a broad spectrum of transportation applications. Along with supplemental files, there is an associated publication, NCHRP Web-Only Document 404: Implementing and Leveraging Machine Learning at State Departments of Transportation, which documents the overall research effort.

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