Application of the Traffic Fundamental Diagram to Assess Detector Performance

This study develops new methods for evaluating detector health via event-based outputs and existing traffic flow theory. In this work, event-based detector data outputs were used to develop empirical vehicle volume-density curves per Greenshields fundamental model. Through integration, these empirical lines were compared with a conceptual volume-density curve for each detector, which was generated with average headway and posted speed limit data. The detector performance and site information were also used to model a predicted volume-density relationship for each detector on the basis of empirical observations, which was then compared with the conceptual line in the same manner as the empirical lines. The outcomes of each comparison were then used to create a database for assessing detector health within the structure of an algorithm. The algorithm is presented and discussed, followed by directions for future research, applications for practice, lessons learned, and limitations of this work.

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Publication Details

Journal
Journal of Intelligent and Connected Vehicles
Published
2024-12-01
DOI
https://doi.org/10.26599/jicv.2023.9210050
Citations
2
Primary Topic
Distributed Sensor Networks and Detection Algorithms
Type
article
Field-Weighted Citation Impact
0.70
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article

Application of the Traffic Fundamental Diagram to Assess Detector Performance

Edward J. Smaglik, Brendan J. Russo, Steven R. Gehrke, David Hurwitz et al.
2 citations
Journal of Intelligent and Connected Vehicles
Distributed Sensor Networks and Detection Algorithms
0.70
article

Application of the Traffic Fundamental Diagram to Assess Detector Performance

Edward J. Smaglik, Brendan J. Russo, Steven R. Gehrke, David Hurwitz, Katherine Riffle, Steven Procaccio
article en
2 citations

Abstract

This study develops new methods for evaluating detector health via event-based outputs and existing traffic flow theory. In this work, event-based detector data outputs were used to develop empirical vehicle volume-density curves per Greenshields fundamental model. Through integration, these empirical lines were compared with a conceptual volume-density curve for each detector, which was generated with average headway and posted speed limit data. The detector performance and site information were also used to model a predicted volume-density relationship for each detector on the basis of empirical observations, which was then compared with the conceptual line in the same manner as the empirical lines. The outcomes of each comparison were then used to create a database for assessing detector health within the structure of an algorithm. The algorithm is presented and discussed, followed by directions for future research, applications for practice, lessons learned, and limitations of this work.

Journal of Intelligent and Connected VehiclesVol. 7(4)
Oregon State University (US), Northern Arizona University (US), Pennsylvania Department of Transportation (US)
Openalex Percentile: Top 25%
Distributed Sensor Networks and Detection Algorithms
0.70
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Application of the Traffic Fundamental Diagram to Assess Detector Performance — Edward J. Smaglik, Brendan J. Russo, et al. · Journal of Intelligent and Connected Vehicles (2024) | TGRS Research Map | TGRS