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.

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.

In the United States, approximately 1,100 people die and 40,000 people are injured annually as a result of motor vehicle crashes in work zones. These numbers may be a result of interruption to regular traffic flow caused by closed traffic lanes, poor traffic management within work zones, general misunderstanding of problems associated with work zones, or improper usage of traffic control devices. In regard to safety of work zones, this study was conducted to identify characteristics and risk factors associated with work zone crashes in Iowa, Kansas, Missouri, Nebraska and Wisconsin, states currently included in the Smart Work Zone Deployment Initiative (SWZDI) region. The study was conducted in two stages. In the first stage, characteristics and contributory causes related to work zone crashes such as environmental conditions, vehicles, crashes, drivers, and roadways were analyzed for the five states for the period 2002-2006. An analysis of percentage-wise distributions was carried out for each variable based on different conditions. Results showed that most of the work zone crashes occurred under clear environmental conditions as during daylight, no adverse weather, etc. Multiple-vehicle crashes were more predominant than single-vehicle crashes in work zone crashes. Primary driver-contributing factors of work zone crashes were inattentive driving, following too close for conditions, failure to yield right of way, driving too fast for conditions, and exceeding posted speed limits within work zones. A test of independency was performed to find the relation between crash severity and other work zone variables for the combined states. In the second stage, a statistical model was developed to identify risk factors associated with work zone crashes. In order to predict injury severity of work zone crashes, an ordered probit model analysis was carried out using the Iowa work zone crash database. According to findings of the severity model, work zone crashes involving trucks, light duty vehicles, vehicles following too close, sideswipe collisions of same-direction vehicles, nondeployment of airbags, and driver age are some of the contributing factors towards more severe crashes.

ABSTRACT: Work zone tends to cause hazardous conditions for drivers and construction workers since work zones generate conflicts between construction activities and the traffic, therefore aggravate the existing traffic conditions and result in severe traffic safety and operational problems. To address the influence of various factors on the crash severity is beneficial to understand the characteristics of work zone crashes. The understanding can be used to select proper countermeasures for reducing the crash severity at work zones and improving work zone safety. In this dissertation, crash severity models were developed to explore the factor impacts on crash severity for two work zone crash datasets (overall crashes and rear-end crashes). Partial proportional odds logistic regression, which has less restriction to the parallel regression assumption and provides more reasonable interpretations of the coefficients, was used to estimate the models. The factor impacts were summarized to indicate which factors are more likely to increase work zone crash severity or which factors tends to reduce the severity. Because the speed variety is an important factor causing accidents at work zone area, the work zone speed profile was analyzed and modeled to predict the distribution of speed along the distance to the starting point of lane closures. A new learning machine algorithm, support vector regression (SVR), was utilized to develop the speed profile model for freeway work zone sections under various scenarios since its excellent generalization ability. A simulation-based experiment was designed for producing the speed data (output data) and scenario data (input data). Based on these data, the speed profile model was trained and validated. The speed profile model can be used as a reference for designing appropriate traffic control countermeasures to improve the work zone safety.

Modeling crash severity is an important component of reasoning about the issues that may affect highway safety. A better understanding of the factors underlying crash severity can be used to reduce the degree of crash severity injury, locate road hazardous sites, and adopt suitable countermeasures. In order to provide insights on the mechanism and behavior of the crash severity injury, a variety of statistical approaches have been utilized to model the relationship between crash severity and potential risk factors. Many of the traditional approaches for analyzing crash severity are limited in that they are based on the assumption that all observations are independent of each other. However, given the reality of vehicle movement in networked systems, the assumption of independence of crash incidence is not likely valid. For instance, spatial and temporal autocorrelations are important sources of dependency among observations that may bias estimates if not considered in the modeling process. Moreover, there are other aspects of vehicular travel that may influence crash severity that have not been explored in traditional analysis approaches. One such aspect is the roadway visibility that is available to a driver at a given time that can impact their ability to react to changing traffic conditions, a characteristics known as sight distance. Accounting for characteristics such as sight distance in crash severity modeling involve moving beyond statistical analysis and modeling the complex geospatial relationships between the driver and the surrounding landscape. To address these limitations of traditional approaches to crash severity modeling, this dissertation first details a framework for detecting temporal and spatial autocorrelation in crash data. An approach for evaluating the sight distance available to drivers along roadways is then proposed. Finally, a crash severity model is developed based upon a multinomial logistic regression approach that incorporates the available sight distance and spatial autocorrelation as potential risk factors, in addition to a wide range of other factors related to road geometry, traffic volume, driver's behavior, environment, and vehicles. To demonstrate the characteristics of the proposed model, an analysis of vehicular crashes (years 2013-2015) along the I-70 corridor in the state of Missouri (MO) and on roadways in Boone County MO is conducted. To assess existing stopping sight distance and decision sight distance on multilane highways, a geographic information system (GIS)-based viewshed analysis is developed to identify the locations that do not conform to AASHTO (2011) criteria regarding stopping and decision sight distances, which could then be used as potential risk factors in crash prediction. Moreover, this method provides a new technique for estimating passing sight distance along two-lane highways, and locating the passing zones and no-passing zones. In order to detect the existence of temporal autocorrelation and whether it's significant in crash data, this dissertation employs the Durbin-Watson (DW) test, the Breusch-Godfrey (LM) test, and the Ljung-Box Q (LBQ) test, and then describes the removal of any significant amount of temporal autocorrelation from crash data using the differencing procedure, and the Cochrane-Orcutt method. To assess whether vehicle crashes are spatially clustered, dispersed, or random, the Moran's I and Getis-Ord Gi* statistics are used as measures of spatial autocorrelation among vehicle incidents. To incorporate spatial autocorrelation in crash severity modeling, the use of the Gi* statistic as a potential risk factor is also explored. The results provide firm evidence on the importance of accounting for spatial and temporal autocorrelation, and sight distance in modeling traffic crash data.

Identifying factors that affect crash injury severity and understanding how these factors affect injury severity is critical in planning and implementing highway safety improvement programs. Factors which can be categorized in to classes such as driver-related, traffic, environmental and geometric design were considered to develop a statistical model that can be used to predict the effects of these factors on severity of injuries sustained from crashes. Police-reported crash data obtained from the Ohio Department of Public Safety (ODPS) at selected freeway merging and diverging areas in the State of Ohio was used for the development of the model. A generalized ordinal logit model or partial proportional odds model was applied to identify the factors that tend to increase the likelihood of one of five levels of injury severity: No Injuries, Possible/Invisible Injuries, Non-incapacitating Injuries, Incapacitating Injuries, or Fatal Injuries.

ABSTRACT: In USA, despite recent efforts to improve work zone safety, the number of crashes and fatalities at work zones has increased continuously over several past years. For addressing the existing safety problems, a clear understanding of the characteristics of work zone crashes is necessary. This thesis summarized a research study focusing on work zone traffic crash analysis to investigate the characteristics of work zone crashes and to identify the factors contributing to injury severity at work zones. These factors included roadway design, environmental conditions, traffic conditions and vehicle/driver features. Especially, special population groups, which divided into older, middle Age, and young, were inspected. This study was based on history crash data from the Florida State, which were extracted from the Florida CAR (Crash Analysis Reporting) system. Descriptive statistics method was used to find the characteristics of crashes at work zones. After then, an injury severity predict model, using the ordered probit regression technology, was developed to investigate the impacts of various factors on different the injury severity at work zones. From the model, it can be concluded that some factors, including the road section with curve, alcohol/drugs involved, a high speed, angle crash and too young or old drivers are more likely to increase the probability of angle crashes. Based on the magnitudes of the variable coefficients, the factor of maximum posted speed have a great impact to injury severity, which shows restriction to driving speed is principle countermeasure for improving work zone safety.