Wrong-way driving (WWD) can result in severe crashes. By responding quickly to WWD dispatch calls, law enforcement officers (LEOs) could stop the wrong-way vehicle before a crash occurs. This paper analyzed law enforcement (LE) response times to WWD dispatch calls in Florida between January 2003 and April 2018 to determine significant effects. The average LE response time was much lower for 2013 onward than before 2013. Average response time was lower during nighttime and in urban areas and was higher for county roads and toll roads. Two ordinal logit models were also developed. These models found that dispatch calls closer to regional traffic management centers or rest areas, in urban areas, or on state roads or local roads typically had lower response times than calls not in these locations. In addition, WWD dispatch calls on toll roads had lower response times than calls on non-toll limited access facilities. Intelligent transportation system (ITS) WWD countermeasures with flashing signs, detection devices, cameras, and direct communication with traffic management centers also help LEOs respond quickly to detected WWD events and more accurately identify the vehicle’s location. As of June 2018, these technologies located at 70 toll road exit ramps in Florida have prompted 307 wrong-way drivers to turn around, possibly preventing nine crashes and saving LEOs over 116 h. The results of this research can help identify locations where increasing LEO presence or installing ITS WWD countermeasure technologies could help reduce WWD response time and WWD crashes, potentially saving lives.

Illegal U-turns on freeways and toll roads are risky maneuvers that sometimes result in the turning vehicles causing various types of collisions or disturbances to approaching traffic. These illegal U-turn maneuvers can occur at traversable grass medians and emergency crossovers. Limited literature was found regarding the impact of illegal U-turns on these facilities. Therefore, to understand the roadway and median characteristics that could influence drivers’ propensity to commit illegal U-turns, a sequential modeling methodology was adopted. This methodology combined a Poisson regression model with a Least Absolute Shrinkage and Selection Operator (LASSO) regression procedure to predict the cited violations at traversable median segments. Additionally, a logistic regression model was developed to predict the probability of a cited violation at official use only emergency crossovers. These models included illegal U-turn citations and crashes for the Orlando and Miami metropolitan areas in Florida from 2011 to 2016. The findings indicated that the average distance between access points, median width, speed limit, segment length, and distance to nearest segment were significant in predicting cited violations at traversable medians. Furthermore, the distance to the nearest interchange, distance to the nearest adjacent crossover, and median width were significant in predicting the probability of a cited violation occurring at an emergency crossover. This study helps agencies to predict the locations of illegal U-turn violations and to prioritize roadways for possible treatment to minimize the potential risk of head-on or other collisions due to illegal U-turn events.