Evaluation of relationship of seat belt use between front seat passengers and their drivers in Dayton, Ohio
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.