TY - GEN
T1 - Integrating Traffic Datasets for Evaluating Road Networks
AU - Gupta, Ariel
AU - Bansal, Ajay
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/9
Y1 - 2018/4/9
N2 - 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.
AB - 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.
KW - Data Cleaning
KW - Data Extraction
KW - Data Integration
KW - Statistical Analysis
UR - https://www.scopus.com/pages/publications/85048444585
UR - https://www.scopus.com/pages/publications/85048444585#tab=citedBy
U2 - 10.1109/ICSC.2018.00081
DO - 10.1109/ICSC.2018.00081
M3 - Conference contribution
AN - SCOPUS:85048444585
T3 - Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018
SP - 411
EP - 416
BT - Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Semantic Computing, ICSC 2018
Y2 - 31 January 2018 through 2 February 2018
ER -