Applying genetic analysis techniques to pedestrian crash data
| Author(s) | Rosica, Megan | |
| Date Accessioned | 2017-12-07T15:23:30Z | |
| Date Available | 2017-12-07T15:23:30Z | |
| Publication Date | 2017 | |
| SWORD Update | 2017-09-05T16:30:36Z | |
| Abstract | No single collision is identical to another: like the structure of DNA, each crash involves a specific sequence of events and distinct characteristics. Current crash data analysis tools and visualization techniques focus on specific factors of crash events, but difficulties arise in attempts to create a single visualization to show all characteristics. The goal of this research is to analyze and visualize a large data set that includes multiple aspects of pedestrian crash data in the State of Delaware. The data set included individual crash factors as well as roadway traits. Even though this state is one of the smallest in America, Delaware continues to have extremely high rates of pedestrian fatalities year after year. By using a genetic based evaluation of pedestrian crash data in Delaware, conclusions can be drawn about possible causes of pedestrian injuries and fatalities, and can aid in the prevention of these incidents occurring. | en_US |
| Advisor | Lee, Earl E., II | |
| Degree | M.C.E. | |
| Department | University of Delaware, Department of Civil and Environmental Engineering | |
| Unique Identifier | 1014180857 | |
| URL | http://udspace.udel.edu/handle/19716/21771 | |
| Language | en | |
| Publisher | University of Delaware | en_US |
| URI | https://search.proquest.com/docview/1957965903?accountid=10457 | |
| Keywords | Applied sciences | en_US |
| Title | Applying genetic analysis techniques to pedestrian crash data | en_US |
| Type | Thesis | en_US |