In recent years, the Intelligent Transportation Systems (ITS) have gained more and more attention from researchers and are widely applied in many transportation fields. Since ITS focuses on processing transportation information and data, data collection is fundamental and essential for the whole system. Most of the data, especially traffic data in real-time, are collected with traffic sensors. Compared with freeways, signalized intersections have much more complicated traffic conditions and deserve more research on data collection. Intersection traffic analysis parameters, such as control delay, queue length, and signal cycle failure, are difficult to directly capture with traffic sensors. In this research, an algorithm is developed to measure these traffic parameters at signalized intersections with traffic count data collected with traditional traffic sensors. Control delay and queue length are measured in a zone with traffic sensors on both ends. A system using video image processing was also developed for locations with no other traffic sensors but video cameras. Performances of these systems were demonstrated with both real-world and simulation data. With the method and system developed by this research, intersection performance can be quantitatively monitored in real-time and this can benefit many transportation applications. One application example of the system discussed in this dissertation is to evaluate the Transit Signal Priority (TSP) system based on traffic sensor inputs.