| With the development of ITS technology, according to the analysis of high quality traffic data, it is discovered that many classical traffic flow theories do not coincide with the actual real-time data. Meanwhile, new phenomena observed is hard to be logically explained by the traditional traffic flow theories. Thus, new theories are required to guide the practical traffic. Under this circumstance, research on traffic theory attracts more and more attention. Presently, large numbers of traffic theories and models are developed to explain complex non-linear traffic phenomena by the scientists all over the world.Due to spatio-temporal causality and stochastic factors, actual traffic system is almost uncertain. Sometimes, the state of traffic system altered dramatically accompanying the change of time and space. An effective traffic model will not only allow for various traffic phenomena but also take driver's behavioral factors into account. It's well known that driver's behavioral factors are very difficult to study. In terms of system engineering, traffic system is a typically complex system. For above reasons, purely quantitative methods are in a dilemma for coping with such a complex system. The dilemma of quantitative methods for modeling traffic system leads more and more scientists to emphasize the qualitative and quantitative methods. To be specific, qualitative reasoning within the area of artificial intelligence is one of great importance thereof.Based on the empirical traffic data of Beijing Second Ring Road (BSRR), the paper provided an experimental analysis combining qualitative process theory with traffic flow theory. Firstly, we analyzed the traffic flow data collected by two typical detectors in BSRR, put forward a qualitative process model based on empirical data and the experimental analysis thereof. Furthermore, the paper analyzed one typical road section with an effective bottleneck in rush hours, and provided a qualitative process model accordingly.
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