Selected Research

Analysis of Crash Location and Crash Severity Related to Road Work Zones in Ohio

Due to growth of vehicle travel using street and highway systems in the state of Ohio, pavement repair and rehabilitation projects have increased over time. 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(s) within the work zone locations. The work zone segment area is made of : (1) termination area (TA), (2) before the first work zone warning sign area (BWS), (3) advance warning area (AWA), (4) transition area (TSA), and (5) 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 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 crash type, posted speed limit, vehicle type, speed related, alcohol related, semi-truck related, youth related and road condition. In the 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.

Analysis of Factors Affecting Motorcycle-Motor Vehicle Crash Characteristics

Worldwide, motor vehicle crashes lead to death and disability as well as financial costs to both society and the individuals involved. Motor vehicle crashes may result in injury, death, and property damage. A number of factors contribute to the risk of a motor vehicle crash, including vehicle design, speed of operation, road design, road environment, driver skill and/or impairment, and driver behavior. The objective of this study was to analyze crash data of motorcycle-motor vehicle collisions to identify possibly influential factors that cause these crashes and to study the magnitude of influence of each factor to these crashes. This study tested appropriate regression models to accurately model the factors that significantly influence motorcycle-motor vehicle crashes.  A nominal multinomial logistic regression model was built. From stepwise selection procedure, the influential factors included age, time of crash, number of units, vehicle in error, road contour, collision type, alcohol used, posted speed, and helmet used. Number of units involved in a crash impacts the crash severity level, such as two units mostly result in injury and three or more units mostly result in fatal. If the driver of the motor vehicle causes the crash it will more likely result into injury than if the driver of the motorcycle causes the crash. Driver of motorcycle or vehicle that uses alcohol will certainly increase the chance of a fatality or injury. Crashes that occur on highways or freeways with higher speed limits are more likely to result in injuries and fatalities. The occupants of motorcycle use helmet will significantly be protected in the crash. These factors can be applied to reduce the severity of motorcycle-motor vehicle crashes.

Characteristics of Drivers Who Cause Run-Off-Road-Crashes on Ohio Roadways

A vehicle that leaves its travel lane at a non-intersection location and collides with another vehicle or with a fixed object or overturns is considered to be involved in a run-off-road (ROR) crash. ROR crashes also known as roadway departure crashes, and these include head-on crashes, crashes that occur due to lane shifts, and crashes where the vehicle leaves its designated travel lane. The main objective of this study was to identify the significant factors that lead to these types of crashes. Crash data used in this study were obtained from the Ohio Department of Public Safety for a five-year period from 2008 to 2012.

The classification tree modeling was used in this study to investigate the significant predictor variables of crash severity of ROR crashes. In addition, this study developed two models, the ROR crashes model and the non-run-off-road (NROR) crashes model. The NROR crashes model used crash data for drivers who were at fault when their crash incidents occurred and for ROR crashes it was assumed that all drivers in this category were at fault of causing their crashes. The ROR model identified nine variables, which include road condition, collision type, alcohol related, posted speed limit, speed related, crash type, vehicle type, gender, and age. The NROR crashes model has six significant predictor variables including collision type, posted speed limit, speed related, road condition, alcohol related, and vehicle type.

Evaluation of Relationship of Seat Belt Use between Front Seat Passengers and Their Drivers in Dayton, Ohio

Several studies have determined the use of seat belts to be one of the major contributing factors in the reduction of fatalities and injury severities associated with motor vehicle crashes. Some studies have found that there is a relationship between drivers and their front passengers in terms of seat belts usage. The objective of this study was to evaluate the seat belts usage rates in Dayton, Ohio based on vehicle type, gender, age, day of the week, time of observation, and person type, i.e.,.  driver or passenger. Data for  this study was collected from thirteen sites in Greater Dayton, Ohio by direct observations at interchange ramps and intersections. The binary logistic regression model was used to investigate some independent variables of seat belt usage rates of drivers and their outboard (front seat) passengers. That is, the binary logistic regression model was used to identify factors that may play a role in relation to seat belt usage. The results from the binary logistic regression modeling show that the person type and vehicle type are significant factors affecting the likelihood of seat belt usage. There were no significant interactions identified between the factors studied. The odds of using seat belt by drivers are higher than the odds of using seat belt by their passengers. Also, the odds of occupants of passenger cars and sport utility vehicles to be belted are higher than the odds of using seat belt by pickup truck occupants. There is no statistically significant difference between van and pickup truck occupants in terms of their seat belt use. Moreover, the pickup truck and van occupants have the lowest seat belt usage rates.     In order to increase seat belt usage rates, this study recommends for enforcement officials to pay more attention with pickup truck and van occupants when checking out unbelted vehicle occupants. This persistence will make them increase their seat belt usage, which eventually will increase their chances of saving their lives in case they get involved in severe crashes. The drivers should be encouraged to persuade their passengers to use seat belts.  The seat belt law should be upgraded to a primary law from the current ineffective (i.e., difficult to enforce) secondary law if the state of Ohio seriously wants to increase the seat belt usage in the state.

Characteristics of Injury and Fatality of Run-Off-Road Crashes on Ohio Roadways

A run-off-road (ROR) crash or a roadway departure crash is a non-intersection crash which occurs after a vehicle crosses an edge line or a center line (i.e., leaves its designated traveled way lane and in the process the vehicle collides with a non-traversable obstacle or another vehicle traveling in the opposite direction or hits a pedestrian, or the vehicle overturns. The main objective of this study study was to determine the factors that contribute significantly to the levels of injury severity when ROR crashes occur. This study used a 5-year crash data for years 2008-2012 obtained from the Ohio Department of Public Safety. The decision tree model in conjunction with generalized ordered logit model was used to investigate characteristics of injury and fatality of run-off-road crashes in Ohio. The decision tree modeling was used for exploratory data analysis and it identified eight factors that explain a large amount of the variation in the response variable, i.e., injury severity. These important predictors for injury severity include road condition, run-off-road (ROR) crash type, posted speed limit, vehicle type, gender, alcohol-related, road contour, and drug-related. Also, complex interactions between parameters were identified. The parameter estimate results from the generalized ordered logit regression model show that the following are significant factors in increasing the likelihood of ROR injury severity levels: alcohol and drugs use, curves and grades, female vehicle occupants, overturn/rollover crashes, ROR crashes occurring on roadway with dry surface conditions. Additionally, buses, trucks, and emergency vehicles, and ROR crashes on roadways with posted speed limits of 40 mph or higher increase the probability of injury severity.