Transit accessibility, the degree to which individuals can access opportunities (eg. jobs or grocery stores) by transit in a given timeframe, has been well studied in American cities in an effort to promote transportation equity. However, there is often a discrepancy between the findings of studies utilizing system-level data such as scheduled travel times (processes) and the actual experiences of transit users (outcomes). Studies of transit outcomes have shown various additional factors, including unreliability, safety, and comfort, to greatly impact the degree to which one can access opportunities via transit. I therefore propose a new method for evaluating transit accessibility that integrates process-based methods with user outcomes in order to more accurately identify transit gaps and inequities. The implementation of this new method is demonstrated by creating a model that calculates transit accessibility using schedule data from regional transit agencies in Chicago (using the General Transit Feed Specification (GTFS)), weighted by official spatial data on transit system performance, customer satisfaction, walkability, and other transit outcome metrics. By comparing the results of this model with another model that utilizes unweighted schedule data alone, I will identify locations in which traditional methods may under- or overestimate transit access and characterize them by their demographics to highlight any equity issues this could cause. My preliminary results concerning walkability suggest that traditional, process-based methods greatly overestimate transit access in areas with poor pedestrian infrastructure and land use, which in Chicago tend to be low-income, majority-Black areas. This innovative method will therefore not only provide a more accurate transit access measure, but can additionally highlight issues of equity that might otherwise be overlooked under traditional approaches, thus allowing decision makers to plan transportation improvements in a manner that better meets the needs of underserved populations.