Authors:
Dhruv Mahajan
1
;
Tania Banerjee
1
;
Anand Rangarajan
1
;
Nithin Agarwal
2
;
Jeremy Dilmore
3
;
Emmanuel Posadas
4
and
Sanjay Ranka
1
Affiliations:
1
Department of Computer Science & Engineering, University of Florida, Gainesville and U.S.A.
;
2
Transportation Institute, University of Florida, Gainesville and U.S.A.
;
3
Florida Department of Transportation, Deland and U.S.A.
;
4
City of Gainesville, Gainesville and U.S.A.
Keyword(s):
ATSPM, Clustering, Classification, Signal Performance, Intersection.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Traffic signals are installed at road intersections to control the flow of traffic. An optimally operating traffic signal improves the efficiency of traffic flow while maintaining safety. The effectiveness of traffic signals has a significant impact on travel time for vehicular traffic. There are several measures of effectiveness (MOE) for traffic signals. In this paper, we develop a work-flow to automatically score and rank the intersections in a region based on their performance, and group the intersections that show similar behavior, thereby highlighting patterns of similarity. In the process, we also detect potential bottlenecks in the region of interest.