Automatic Passenger Counting (APC) and rider Origin-Destination (OD) flow tracking could be used to provide insights into transit system utilization and travel patterns, in turn minimizing costs and increasing efficiency of the transit system. Traditionally, passenger counting is accomplished manually, making it costly and prone to errors. There are several non-trivial issues with current, stand-alone COTS APC sensors, both for accurate passenger counting and OD flow estimation. Given existing or even improved APC computing, through statistical multi-sensor fusion, a major challenge is to track the origin and destination of riders by deriving route-level passenger OD flow matrices. The relatively recent widespread adoption of APC and Automatic Fare Collection (AFC) technologies, by transit agencies, has opened up the possibility of using these large and existing datasets amassed to estimate route OD flows. IAI, and its collaborators from Ohio State University (OSU), will design and develop an integrated multi-sensor passenger detection system for both APC and OD traffic flow estimation. This system will collect APC data from multiple sensors, fusing them together to create a better estimate of people in the vehicle. Next, the system will use information from surveys and a WiFi/Bluetooth sensor to infer OD flows. It will take the data output from APC Estimation and OD Estimation for mining purposes such as bus route optimization. The IAI system will be demonstrated at OSU's “Living Lab.” The availability of the “Living Lab” will greatly reduce the risk of the IAI system and allow both data collection and experiments to be conducted at unprecedented scales.