Due to growth of vehicle travel using streets and highway systems in the United States, pavement repair and rehabilitation projects have increased. As a result, the presence of work zones has created traffic congestion and has increased the crash risk. The main object of this study was to identify significant factors that contribute to an increase in crash severity in the state of Ohio and recognize the most risk segment within the work zone locations. The work zone segment area is made of : (a) termination area (TA), (b) before the first work zone warning sign area (BWS), (c) advance warning area (AWA), (d) transition area (TSA), (e) activity area (AA). This study used a 5-year crash data from Ohio Department of Public Safety (ODPS) database from 2008 to 2012. In this study, classification tree modeling was used to investigate significant predictor variables of crash severity of work zone related crashes and recognize the most significant crash location within work zone areas in the state of Ohio. Classification tree modeling identified ten important variables (factors) that explain a large amount of the variation in the response variable, crash severity. These predictor variables of work zone crash severity identified include collision type, motorcycle related, work zone crashes type, posted speed limit, vehicle type, speed related, alcohol related, semi-truck related, youth related and road condition. In case of work zone location analysis results, this study identified six significant factors, which include collision type, work zone crash type, posted speed limit, vehicle type, workers present, and age of driver. Collision type is the most significant factor that affects crash severity in a work zone. Likewise, for work zone location, the work-zone crash type was the most significant factor that contributed in increasing the probability of work zone location crashes.

Work zones are given priority by most government agencies nationwide because of the need for maintenance, rehabilitation, and advancement of the existing road networks. This results in large number of work zones and has had an inevitable impact on the regular traffic flows and traffic safety issues. The main objective of the study is to find the risk factors affecting crash severity (dependent variable) in work zones in the state of Ohio. Year 2010 data was collected from Ohio Department of Traffic Safety in the form of an excel spreadsheet. A total of 24 different independent variables which has 12,275 crash records were used in the development of the Crash Severity Model (CSM). The following steps were employed for the development of CSM. First, the Pearson chi-square statistics test was done to find the variables that have a significant relationship among themselves and the dependent variable. Second, the insignificant variables left from step 1 were selected which were found to have significant effect on crash severity in other studies and they were also added along with the significant variables found in step 1 for the development of the regression model. A total of 21 variables were found to have a significant relationship with the dependent variable. Three variables were selected from step 2 based on literatures. A binomial logit model was used to predict crash severity. Results of binary logistic regression showed that forty four categories of seventeen variables were found to be predictive of the fatal/injury crash severity type and also showed that the model fits to the data well with a prediction ability of 72.8 percent.

"The safe and efficient flow of traffic through work zones must be established by improving work zone conditions. Therefore, identifying the factors associated with the severity and the frequency of work zone crashes is important. According to current statistics from the Federal Highway Administration, 2,372 fatalities were associated with motor vehicle traffic crashes in work zones in the United States during the four years from 2010 to 2013. From 2002 to 2014, an average of 1,612 work zone crashes occurred in Kansas each year, making it a serious concern in Kansas. The objectives of this study were to analyze work zone crash characteristics, identify the factors associated with crash severity and frequency, and to identify recommendations to improve work zone safety. Work zone crashes in Kansas from 2010 to 2013 were used to develop crash severity models. Ordered probit regression was used to model the crash severities for daytime, nighttime, multi-vehicle and single-vehicle work zone crashes and for work zones crashes in general. Based on severity models, drivers from 26 to 65 years of age were associated with high crash severities during daytime work zone crashes and driver age was not found significant in nighttime work zone crashes. The use of safety equipment was related to reduced crash severities regardless of the time of the crash. Negative binomial regression was used to model the work zone crash frequency using work zones functioned in Kansas in 2013 and 2014. According to results, increased average daily traffic (AADT) was related to higher number of work zone crashes and work zones in operation at nighttime were related to a reduced number of work zone crashes. Findings of this study were used to provide general countermeasure ideas for improving safety of work zones" (page ii).

Work zone presence is an important concern for drivers in terms of safety and congestion. In recent years, work zone safety has received much attention due to numerous highway renovation projects that have resulted in many work zone crashes. To minimize the effect of work zones on roadway safety risks and traffic conditions, potential factors need to be addressed and countermeasures need to be implemented to ensure that the motorist can drive in a safe manner. The impact of the work zones can be estimated by using descriptive analysis and different statistical modeling methods. To this end, this study focused on three major areas: the crash frequency at work zones, the crash severity at work zones and the change in traffic conditions at work zones. Statistically robust models were developed by incorporating integrated datasets that could identify significant factors affecting each of these study areas. To better understand this, different from the previous studies, model results were compared against reference conditions, such as work zone crash frequency and modeling parameters were compared with non-work zone parameters. In addition, different statistical modeling techniques were applied to examine the best model or set of variables to connect crash severity and possible causative factors for binary level and multiple level outcomes. Two crash severity indexes were proposed and used to estimate multilevel crash severity by using both maximum severity and the monetary cost weighted severity. Besides safety issues, different types of lane closures and crashes observed within lane closures were studied to examine if there would be a change in traffic conditions compared with normal time traffic. Comparisons of each concept provides an idea for agencies about the differences of work zone and non-work zone conditions which is important if indeed there is a specific impact for the work zone cases. Work zone presence was found to have an increasing effect on crash occurrence. Nighttime shifts were found to be safer when compared to daytime work zone periods. Injury crashes for two-lane closure cases were found to have a more marked impact on traffic volume compared with other cases studied in this dissertation. In the conclusions chapter, all of these findings are summarized along with specific recommendations.

Truck safety is a very crucial aspect of the overall safety of the transportation system. Statewide there has been a significant increase in the probability of trucks being involved in crashes, primarily due to the fact that the total number of registered trucks, as well as the truck vehicle-miles traveled, have both increased within the last 10 years. Recognizing the substantial impact of truck-related crashes in the overall transportation safety, this study attempted to identify the contributing factors that influence the increase in truck-related crash severity, using truck-related crash data for the last two and half years (July 2013-December 2015) that were obtained from the Ohio Department of Public Safety Traffic (ODPS). This thesis study used the classification tree model to investigate the important factors affecting injury and fatality related to truck crashes in Ohio. Eighteen independent variables that represent various driver, roadway, environmental and crash characteristics were tested in the classification tree model of truck-related crash model. The dependent variable, crash severity was coded as a binary variable, with no injury and injury/fatal as its two crash severity levels. The classification tree model selected five independent variables as the only most significant factors influencing truck-related crash severity. These variables are crash type, posted speed limit, collision event, speed-related and road contour. Their significance is also in that order, with the crash type being the most significant, contributing about 55.8% to the model, posted speed limit contributing about 18.5%, collision event about 17.7%, speed-related about 6.0% and lastly road contour about 2.0%

Work zones tend to cause hazardous conditions for drivers and construction workers since they generate conflicts between construction activities and traffic. A clear understanding of the characteristics of work zone crashes will enhance the selection of the appropriate measures that can minimize the negative impacts of work zones. This study investigated the characteristics of work zone crashes that occurred in Virginia between 1996 and 1999. The information on each crash was obtained from the police crash record. The location of each crash was categorized as being in one of the five areas of a typical work zone: advance warning area, transition area, longitudinal buffer area, activity area, and termination area. An analysis of the percentage distributions was then carried out with respect to area in work zone, severity, type of collision, and type of highway. The proportionality test was used to determine significant differences at the 5 percent significance level. Selected crash characteristics, such as the proportions of single- and multi-vehicle crashes, were compared for work zone and non-work zone crashes. The results indicated that the activity area was the predominant location for work zone crashes regardless of highway type and that rear-end crashes were the predominant type of crash. The results also indicated that the proportion of sideswipe same direction crashes in the transition area was significantly higher than in the advance warning area and that work zone crashes involved a higher proportion of multi-vehicle crashes and fatal crashes than did non-work zone crashes.