Impact of risk factors on work zone crashes using logistic models and Random Forest

Abstract

Work zone safety is influenced by many risk factors. Consequently, a comprehensive knowledge of the risk factors identified from crash data analysis becomes critical in reducing risk levels and preventing severe crashes in work zones. This study focuses on the 2016 severe crashes that occurred in the State of Michigan (USA) in work zones along highway I-94. The study identified the risk factors from a wide range of crash variables characterizing environmental, driver, crash and road-related variables. The impact of these risk factors on crash severity was investigated using frequency analyses, logistic regression statistics, and a machine learning Random Forest (RF) algorithm. It is anticipated that the findings of this study will help traffic engineers and departments of transportation in developing work zone countermeasures to improve safety and reduce the crash risk. It was found that some of these factors could be overlooked when designing and devising work zone traffic control plans. Results indicate, for example, the need for appropriate traffic control mechanisms such as harmonizing the speed of vehicles before approaching work zones, the need to provide illumination at specific locations of the work zone, and the need to establish frequent public education programs, flyers, and ads targeting high-risk driver groups. Moreover, the Random Forest algorithm was found to be efficient, promising, and recommended in crash data analysis, specifically, when the data sample size is small.

Health and Safety in Temporary Work Zone Road Construction Project in Saudi Arabia: Risks and Solutions

“…Interestingly, there was an awareness of the importance of having updated standards and a better database system and analysis as both agreed overall on the fourth and fifth ranks, respectively. Most of the researchers in the literature mentioned that when the WZ crashes data are misclassified, that will lead to a lack of detailed data [ 5 , 17 , 30 , 31 , 36 ]. Additionally, they suggested the development of uniform standards for reporting WZ crashes and shared data to better facilitate work zone crashes investigation.…”

“…Safety in road work zones (RWZs) remains a high-priority issue for roadway agencies mainly due to the limited understanding of the contributing factors of the crashes and the lack of perception of road works’ importance globally and locally. Accordingly, much research has been conducted to investigate the influences of many aspects of road work zone characteristics on RWZ safety [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. The factors that contribute to crash frequency and/or severity include, but are not limited to, location, time and type of crash, roadway environment conditions and characteristics, and human factors.…”

“…Moreover, the majority of studies agree that the most common crash type is the rear-end crash, which is the most predominant crash in RWZs [ 4 , 5 , 8 , 15 , 16 , 27 ]. In terms of crash time frequency, the majority of studies revealed that most crashes occur during the daytime [ 4 , 17 , 19 , 24 , 27 , 29 , 30 ], but crashes that occur at night were found to be more severe [ 4 , 20 , 21 , 24 , 27 , 28 , 30 ].…”

“…In addition, previous studies have made many recommendations that could eliminate the risks of the road work zone and promote safety and mobility in the work zone, for example, the awareness of drivers about road work hazards and how this may affect their daily commute. Direct communication with motorists about precautions that should be taken when driving through WZs using social media campaigns, radio stations, effective educational programs, and seminars for the general public is recommended [ 17 , 21 , 24 , 29 , 30 ], as well as taking strict actions against those who commit safety violations and imposing heavy fines on speeding drivers [ 21 , 31 ]. In addition, using a cooperative intelligent transport system (C-ITS) for roadwork warnings would reduce bottlenecks at the roadworks’ entrance [ 32 ].…”

Application of Unsupervised Machine Learning Classification for the Analysis of Driver Behavior in Work Zones in the State of Qatar

“…Every 5.4 min a work zone-related crash happens according to the Federal Highway Administration (FHWA) [1]; as such, work zone safety is considered a high priority for research studies and transportation agencies [1][2][3][4][5]. The topic is not only a concern for road travelers but also a serious concern for the workers in the work zones where they are usually at a high risk of being injured in crashes with vehicles.…”