This research aims to develop software tools to support the fusion and analysis of large, passively collected data sources for the purpose of measuring and monitoring transit system performance. This study uses San Francisco as a case study, taking advantage of the automated vehicle location (AVL) and automated passenger count (APC) data available on the city transit system. Because the AVL-APC data are only available on a sample of busses, a method is developed to expand the data to be representative of the transit system as a whole, and to impute missing values. In the expansion process, the General Transit Feed Specification (GTFS) data are used as a measure of the full set of scheduled transit service.

The data mashing tool reports and tracks transit system performance in these key dimensions:

An important characteristic of this study is that it provides a tool for analyzing the trends over a significant time periods—from 2008 through the present. The tool allows data for any two time periods to be queried and compared at the analyst’s request, and puts the focus specifically on the changes that occur in the system, and not just observing current conditions.