Despite much advancement in traffic safety, motor vehicle accidents still account for over 36,000 deaths in the United States and have increased from 1999 to 2015 (National Vital Statistics Reports, 2017). As such, there is a need to improve traffic safety and reduce deaths due to motor vehicle collisions.
Road design is often informed by traffic microsimulations. These simulations are usually based upon driver models and make assumptions regarding the drivers.
Using these models, it is difficult to predict accidents because, on the timescale of the microsimulations, they are relatively rare (Ciro and Maurizio, 2012). Instead, accidents are prevented using standard design practices (Erskine, 2006), but these practices may not be suitable or scalable to certain regions, road conditions, or intersection geometry.
Designers need a tool that can accurately and quickly analyze a road design and identify key areas of high risk for accidents.
According to a study by the World Health Organization, approximately 1.25 million die each year in traffic accidents (Who, 2015). There has been previous research investigating the causes of road crashes, including factors associated with driver behavior and road design features (Thomas, 2013; Wedajo, 2017; Mohamedshah, 1994; Çepni, 2017; Lam, 1995).
William Haddon developed a matrix that identifies the risk factors before the crash, during the crash, and after the crash, in relation to the person, vehicle, and environment (Haddon, 1968). Some accidents were found to occur due to road designs; however, police officials considered these accidents due to drivers’ fatal mistakes (Obaidat, 2012).
Hence, further analysis of crash data in relation to road designs can provide a better understanding of the higher crash rates in certain locations and allow the officials to have a clearer picture of the factors involved in accidents. Policies on highway geometric design in the US are developed by the American Association of State Highway and Transportation Officials (AASHTO). These policies are based on design guidelines by the state highway and transportation departments and the Federal Highway Administration (FHWA).
Guidelines for highway geometric design are presented in A Policy on Geometric Design of Highways and Streets (AASHTO, 2001).
Previous studies investigated various factors associated with road design features (e.g., intersections) or roadside features (e.g., the size and type of advertising signs) (Ziegler, 1951). Roadway curves were found to have five times more accidents than straight roadways, where sharper roadway curves have higher accident rates than flatter roadway curves (Timothy et al., 1983; Ziegler, 1951).
These findings motivated further simulation studies to explore different approaches to lower the rate of car crashes, including the installation of intelligent road systems (e.g., physical intelligent barriers (Massoudian and Eshghi, 2015). Some of the earlier work also reviewed tunnels’ traffic safety, including the impact of tunnel length, fire incidents, and zone of the tunnel (Bassan, 2016; Ma, 2009; Amundsen, 2001; Cornelia, 2007).
Short tunnels had higher crash rates than long tunnels although the chance of crash occurrence is lower in a tunnel than on an open highway section.
Amundsen et al. found that the crash rates are higher in the entrance of tunnels and decreased as drivers continued driving inside the tunnel (Amundsen, 2001). However, a study of Chinese freeway tunnels found an increased number of crashes inside the tunnel in contrast to previous studies (Ma, 2009). Potential reasons were thought to be higher driving speeds and insufficient sight distance, which is required for higher design speeds (Ma, 2009).
We are concerned with assessing driving risk levels for individual road segments and predicting the severity of a traffic accident given that one has occurred.
Relevant features we will investigate include road and intersection design, time of day, traffic density, type or size of road, and regional variations (possibly a result of different driver behavior). For this study, our team will focus solely on traffic data in Chicago.
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