Name
Abstract | ||
|---|---|---|
Improving the safety of roads has traditionally been approached by governmental agencies including the National Highway Traffic Safety Administration and State Departments of Transportation. In past literature, automobile crash data is analyzed using time-series prediction techniques to identify road segments and/or intersections likely to experience future crashes. After dangerous zones have been identified road modifications can be implemented improving public safety. This project introduces a historical safety metric for evaluating the relative danger of roads in a road network. The historical safety metric can be used to update routing choices of individual drivers improving public safety by avoiding historically more dangerous routes. The metric is constructed using crash frequency, severity, location and traffic information. An analysis of publicly available crash and traffic data in Allegheny County, Pennsylvania is used to generate the historical safety metric for a specific road network. Applications of this metric include comparison of routes based on the safety metric that begins with summing the danger of each accident on each street. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICSC.2018.00081 | 2018 IEEE 12th International Conference on Semantic Computing (ICSC) |
Keywords | Field | DocType |
Data Extraction,Data Integration,Data Cleaning,Statistical Analysis | Data integration,Crash,Road networks,Computer science,Transport engineering,Data extraction,Statistical analysis | Conference |
ISSN | ISBN | Citations |
2325-6516 | 978-1-5386-4409-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ariel Gupta | 1 | 0 | 0.34 |
| Ajay Bansal | 2 | 320 | 27.21 |