Document Type
Technical Report
Publication Date
4-21-2022
Keywords
Queue Warning, Queue Detection, Queue Impacts, Queue Data, Queuing Analysis
Digital Object Identifier (DOI)
https://doi.org/10.5038/CUTR-NICR-RR-2022-1-3
Abstract
The main objectives of this study were to identify available data sets and explore methodologies for improving the detection of bottlenecks, related congestion, and queue formation, as well as formulate methodologies to determine the extent and rate of spread queues, identify their impact area, and look at potential mitigation strategies. Methodologies are provided that evaluate (a) impacts of single construction projects, (b) combined daily impacts of construction projects and incidents on selected segments of the corridor, and (c) a process to find the most appropriate schedule that minimizes the negative impact of construction, utility work or special events that require partial or full closure of a roadway. Finally, this study describes an approach to queue detection using a combination of data available from two different sources (traffic sensors and third-party data providers) and finding the best combination of the two data sources for queue detection.
Was this content written or created while at USF?
Yes
Scholar Commons Citation
Pesti, Geza; Storey, Beverly; and Brydia, Robert, "System Monitoring of Auto Traffic: Queue Detection and Congestion Impact Assessment" (2022). Research Reports. 6.
https://digitalcommons.usf.edu/cutr_nicr/6
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