Degree Type
Creative Component
Semester of Graduation
Spring 2020
Department
Civil, Construction, and Environmental Engineering
First Major Professor
Dr. Anuj Sharma
Degree(s)
Master of Science (MS)
Major(s)
Civil Engineering
Abstract
Traffic incidents impact traffic system components and have a major contribution to congestion, delays, and secondary incidents. In order to avoid these issues, all traffic control systems are trying to detect incidents in a timely manner using different sources of traffic monitoring. This research has developed a comparison method among different sources of real-time incident detection in terms of temporal and spatial characteristics. Incident reports from Waze and ATMS and generated automated incidents from the Inrix, and Wavetronix were used for this analysis. The result shows that all data sets have a similar pattern in recording incidents. The number of incidents recorded by Waze, Wavetronix, and Inrix tracks each other closely during different times of the day. ATMS is a validated set of incidents reports, but still, there are more incidents that were not recorded in ATMS. A state-of-the-art matching function is built to get the number of overlapping incidents from all four data sets. Waze and Inrix have the highest overlapping with ATMS, about 57%, which affirms their reliability. The matching function also was used to get the estimated contribution of each source to ATMS. Due to the lack of validation source, there will be some false alarms in the total number of contributions that were not identified.
Copyright Owner
Khalilzadeh Fatemeh
Copyright Year
2020
File Format
Recommended Citation
Khalilzadeh, Fatemeh, "Evaluating the Temporal Coverage, Reliability, and Contribution of Incident Detection Sources Using Big Data Analysis" (2020). Creative Components. 518.
https://lib.dr.iastate.edu/creativecomponents/518
