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There is a lack of prior studies that investigated the relationship between traffic flow variables and traffic crashes that occur due to reduced visibility. This paper aims at exploring the occurrence of visibility related (VR) crashes on freeways using real-time traffic surveillance data (speed, volume and occupancy) collected from underground loop detectors (LD) and radar sensors potentially associated with VR crash occurrence. The research hypothesis here is to compare traffic flow characteristics leading to VR crashes with non-crash cases at reduced visibility conditions. Historical crash and LD data were collected from Interstates 4 and 95 in Florida between December 2007 and March 2009. To achieve the objectives of this study, Random Forests (RF), a relatively recent data mining technique, was used to identify significant traffic flow variables affecting VR crash occurrence. Using significant variables selected by RF, matched case-control logistic regression model was estimated. The purpose of using this statistical approach is to explore the effects of traffic flow variables on VR crashes while controlling for the effect of other confounding variables such as the geometric design elements of freeway sections (ie horizontal and vertical alignments). The results revealed that the 5-minutes average occupancy observed at the nearest downstream station during 10-15 minutes before the crash along with the average speed measured at the downstream and upstream stations during 5-10 minutes before the crash increase the likelihood of VR crash occurrence in between. In addition, by using a threshold value of 1.0 for the corresponding odds ratio, over …