With recent (prepandemic) growth in both transit ridership and the number of passenger rail systems nationwide, researchers have been increasingly interested in quantifying the rail transit loading environment. Research that stemmed from this renewed interest provided engineers with greater insights into the loading demands placed on the track structure of heavy, light, and commuter rail systems. Although results from this earlier work were useful in a general manner, it was not possible to provide agencies with immediately actionable information on wheel loads, since the relevant data were analyzed and reported at a later date. As a result, agencies were unable to monitor their rolling stock wheel health in real time. In addition, trend analysis was not possible because it was not feasible to track specific wheels over time. To address these limitations,researchers at the University of Illinois have developed an economical system that both provides real-time notifications to transit agencies when it detects problematic loading conditions, and tracks specific wheels over time. This paper provides a framework for installing and launching this real-time wheel health monitoring system that transit agencies can replicate, as well as presents some preliminary data that have been collected. By receiving actionable wheel load data and better under-standing the wheel deterioration trends present on their networks, agencies can remove bad actor wheels from service before they damage the track structure, improving the state of good repair. In addition, a more thorough understanding of the loading environment will allow them to plan maintenance and design more effectively.