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Wrong-way crashes on limited access highways continue to be a serious problem for state highway agencies and communities. These crashes are more likely to result in fatalities or serious injuries than other traffic incidents, due to vehicles travelling at high speeds, often involved on a roadway with limited vertical space and time to avoid a crash. This research study focused on wrong-way crashes that occurred on highways in the State of Kansas. Although these crashes represent a very small portion of crashes, wrong-way crashes were found to have a higher rate of fatalities and injuries as compared to other crash types. Using ten years of crash data, it was found that a typical wrong-way crash occurred under no adverse weather conditions, at a non-intersection location, in daylight or with streetlights present, involved alcohol or drugs, and resulted in a fatality or serious injury. An ordinary logistic model was developed to identify significant characteristics of wrong-way crashes. The model indicated that drivers under the influence of alcohol or drugs was found to be a significant in both fatal and injury wrong-way crashes. It was also found that certain lighting conditions were also significant, along with use of safety equipment and drivers over the age of 55 years old. Additional research is needed to further investigate wrong-way crashes and their causalities in Kansas on other roadway facilities including rural at-grade divided intersections.

Objective: Despite recent improvements in highway safety in the United States, serious crashes on curves remain a significant problem. To assist in better understanding causal factors leading to this problem, this article presents and demonstrates a methodology for collection and analysis of vehicle trajectory and speed data for rural and urban curves using Z-configured road tubes., Methods: For a large number of vehicle observations at 2 horizontal curves located in Dexter and Ames, Iowa, the article develops vehicle speed and lateral position prediction models for multiple points along these curves. Linear mixed-effects models were used to predict vehicle lateral position and speed along the curves as explained by operational, vehicle, and environmental variables. Behavior was visually represented for an identified subset of "risky" drivers., Results: Linear mixed-effect regression models provided the means to predict vehicle speed and lateral position while taking into account repeated observations of the same vehicle along horizontal curves., Conclusions: Speed and lateral position at point of entry were observed to influence trajectory and speed profiles. Rural horizontal curve site models are presented that indicate that the following variables were significant and influenced both vehicle speed and lateral position: time of day, direction of travel (inside or outside lane), and type of vehicle.