To to identify drivers’ decisions of RLR, first the data collected from stop-bar detectors are used to directly identify YLR, RLR, and FSTP cases. The basic idea is very intuitive: if a vehicle approaches stop-bar with a relatively high speed (higher than a threshold value), w e conclude that the driver decides to run through the intersection; otherwise this driver decides to stop. For the running cases, if the signal indication is yellow when the vehicle passes through the stop-bar detector, it is an YLR; and it is a RLR if the signal is red. Note if the stop-bar detector is installed directly behind the stop-bar (such as Int. Rhode Island, see Figure 2), the threshold value is set as 10 mph. But for some intersections like Int. Boone and Int. Winnetka, the detectors are located about 50 feet behind stop-line. Therefore a different threshold value of 20 mph is applied. Here, the threshold values of 10mph and 20 mph are very conservative. The purpose is to ensure that the identifications are correct.

From a statistical point of view, a driver’s current driving conditions (i.e. speed and the time gap between the target and leading vehicle), together with surrounding traffic condition (i.e. the driving behaviors of surrounding vehicles) and signal timing situation (i.e. signal status of green, yellow, or red and their durations), will directly or indirectly lead to the driver’s later decision of RLR . Therefore, by statistically analyzing a large amount of event data, the inner correlation between all these impact factors and driver’s RLR decision can be derived; and such correlation then can be used to predict RLR through some statistical methods, such as Logit model. This is the basic idea of this research. Note the proposed methods are purely based on statistical analysis, which first identifies significant impact factors which impact drivers’ decisions and then derives the correlation between these impact factors and drivers’ decision.