Citation

Al-Sahili O, Al-Deek H, Sandt A, Mantzaris AV, Rogers JH, Faruk MO. Transp. Res. Rec. 2018; 2672(14): 73-84.

Affiliation

Department of Civil, Environmental, and Construction Engineering, University of Central Florida (UCF), Orlando, FL 2Department of Statistics, University of Central Florida (UCF), Orlando, FL Corresponding Author: Address correspondence to Haitham Al-Deek: Haitham.Al-Deek@ucf.edu

Copyright

(Copyright © 2018, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198118778941

PMID

unavailable

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.

Language: en