Investigating and Modeling the Illegal U-Turn Violations at Medians of Limited Access Facilities

Abstract

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.

Factors Affecting Road Traffic: Identifying Drivers of Annual Average Daily Traffic Using Least Absolute Shrinkage and Selection Operator Regression

“…To model the relationship between the independent and dependent variables, we use the least absolute shrinkage and selection operator (LASSO) method ( 44 ) applied on each vehicle type, road class, and subgroup individually. The LASSO method is both a regression and variable selection method that aims to produce a set of statistically significant predictors to minimize the estimation error ( 45 ), and it has been found to perform well when used for both regression and variable selection tasks ( 46 , 47 ). The variable selection is done by imposing a penalty term on the model parameters so that some regression coefficients shrink to zero ( 48 ) and, consequently, these variables are excluded.…”

The Feasibility and Operational Performance of Implementation Median U-turn Intersections: A CRITIC Method

“…While their study primarily focused on predicting and analyzing the contributing factors to crossover crashes resulting from intentional illegal U-turn violations, it provides valuable insights into the safety implications of median U-turns. Understanding the safety aspects is essential when evaluating median U-turns using multi-criteria decision-making, as safety considerations are one of the critical criteria in the decision-making process (Al-Sahili et al, 2018). Furthermore, Dong et al (2015) applied the analytic hierarchy process (AHP) to evaluate intelligent U-turn behavior of unmanned vehicles.…”

Modeling Wrong-Way Driving Entries at Limited Access Facility Exit Ramps in Florida

“…WWD 911 calls, daily vehicle miles traveled (DVMT), percentage of licensed drivers aged between 50 and 65, and percentage of Hispanic population in a county were found to be the most significant influencing factors for WWD crashes in a county ( 18 ). UCF researchers also developed models for predicting different types of citations related to WWD, including median crossover citations ( 18, 19 ). The total number of WWD citations in a county increases with the number of 911 calls, DVMT, and percentage of Hispanic population ( 18 ).…”