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.
Original language | English (US) |
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Title of host publication | Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 411-416 |
Number of pages | 6 |
Volume | 2018-January |
ISBN (Electronic) | 9781538644072 |
DOIs | |
State | Published - Apr 9 2018 |
Event | 12th IEEE International Conference on Semantic Computing, ICSC 2018 - Laguna Hills, United States Duration: Jan 31 2018 → Feb 2 2018 |
Other
Other | 12th IEEE International Conference on Semantic Computing, ICSC 2018 |
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Country/Territory | United States |
City | Laguna Hills |
Period | 1/31/18 → 2/2/18 |
Keywords
- Data Cleaning
- Data Extraction
- Data Integration
- Statistical Analysis
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Human-Computer Interaction
- Information Systems and Management