Survey Across 15 States Found Annual Operating Costs for Machine Learning Applications Were Under $50K in 10 States.

Machine Learning (ML), an emerging field over the past two decades, can “analyze vast datasets, discover patterns, make predictions, and continuously improve through experience represents a paradigm shift compared to traditional rule-based methods that agencies have deployed for decades, and therefore, offering the opportunity to improve transportation system performance and agency operation.” To better help agencies become aware of ML’s potential benefits and challenges, this study aimed to create a guide for state DOTs and other transportation agencies to establish their agency’s ML readiness and capabilities. The study administered a survey to gather insights from 15 state DOTs   on their agencies’ use of ML methods and applications. 

The survey solicited input on agencies’ use of ML methods and applications. The survey responses cover respondents from 29 different states. Fifteen of the 29 states represented in the survey indicated that their agency had ML applications currently deployed and/or being developed. The survey also gathered information on the annual operating cost for each respondent’s agency’s ML application. Among the 15 participants, 10 reported (i.e., 66.7 percent) that their agency spent less or equal to $50,000, two reported costs between $50,000 and $200,000, while three reported their agency’s annual operating cost for ML exceeded $300,000. It is important to note that the costs reported in this report were the estimated operating costs for ML applications currently deployed or being developed, which would be different from pilot project costs.