Abstract
Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent efforts to improve work zone safety, the frequency and severity of work zone crashes are still a big concern for transportation agencies. Although many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity. This paper utilizes probit–classification tree, a relatively recent and promising combination of machine learning technique and conventional parametric model, to identify factors affecting work zone crash severity in adverse weather conditions using 8 years of work zone weather-related crashes (2006–2013) in Washington State. The key strength of this technique lies in its capability to alleviate the shortcomings of both parametric and nonparametric models. The results showed that both presence of traffic control device and lighting conditions are significant interacting variables in the developed complementary crash severity model for work zone weather-related crashes. Therefore, transportation agencies and contractors need to invest more in lighting equipment and better traffic control strategies at work zones, specifically during adverse weather conditions.
Highlights
The widespread aging of the US roads and the increase in traffic demand have raised the need for more transportation maintenance projects; affecting both safety and operations of roadways
Many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity
3023 observations were used and divided among mentioned sub-datasets (i.e., 1813 observations were assigned to the training sub-dataset, 906 observations were assigned to the validation sub-dataset, and 302 observations were assigned to the testing sub-dataset)
Summary
The widespread aging of the US roads and the increase in traffic demand have raised the need for more transportation maintenance projects; affecting both safety and operations of roadways. A number of studies have been conducted on work zone crashes Results from these studies showed that crash rate and frequency are increased by the presence of work zones [2,3,4,5,6]. Debnath et al [8] identified the most frequent hazards at work zones from road-worker perspective They found that speeding vehicles are the common work zone hazard. Wang and Qin [9] investigated the severity of single-vehicle crashes at work zones They concluded that speeding is one of the key factors affecting the severity of work zone crashes. Characteristics of work zone rear-end crashes were analyzed by Qi et al [10], and they found that driving under the influence, lighting condition, the presence of pedestrians, and roadway defects have the highest effects on severity of work zone rear-end crashes
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