Cycle-by-cycle Delay Estimation at Signalized Intersections by using Machine Learning and Simulated Video Detection Data
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- @InProceedings{Erdagi:2023:MT-ITS,
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author = "Ismet Goksad Erdagi and Nemanja Dobrota and
Slavica Gavric and Aleksandar Stevanovic",
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booktitle = "2023 8th International Conference on Models and
Technologies for Intelligent Transportation Systems
(MT-ITS)",
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title = "Cycle-by-cycle Delay Estimation at Signalized
Intersections by using Machine Learning and Simulated
Video Detection Data",
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year = "2023",
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abstract = "Accurate estimation of delay is crucial for efficient
traffic signal operations. Estimation of delay in the
real-time manner using traditional loop detectors
requires advanced detectors (in addition to stop-bar
detection). In cases when this detection layout is not
in place, delay estimates are approximated with a lower
accuracy. Video detection is one of the most frequently
deployed detection systems at signalized intersections
in recent years. In most cases video detection operates
in the same way as traditional inductive loops.
However, when coupled with computer vision algorithms,
video detection systems could be used to retrieve
additional information (e.g., vehicular arrivals and
departures) that cannot be taken out from the
conventional systems (e.g., long stop-bar loop
detectors). Although present for several decades, video
detection data were not frequently examined for delay
estimation purposes. In this study, we proposed a novel
delay estimation model which can be developed with only
data from stop-bar video detectors. Relevant data were
collected from a simulation model of 11 signalized
intersections at downtown Chattanooga, TN and processed
to create needed inputs for model development. With the
use of multigene genetic programming the authors
developed a delay model that outperforms accuracy of
multi regression model. Furthermore, authors evaluated
the developed model by comparison with the other
benchmark delay models, such as HCM and approach delay
model. It was found that the developed MGGP delay model
outperforms benchmark models for a wide range of
traffic and signal operation conditions.",
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keywords = "genetic algorithms, genetic programming, Uncertainty,
Estimation, Detectors, Delay estimation, Machine
learning, Benchmark testing, performance measures,
delay, machine learning, traffic, video detection",
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DOI = "doi:10.1109/MT-ITS56129.2023.10241732",
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month = jun,
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notes = "Also known as \cite{10241732}",
- }
Genetic Programming entries for
Ismet Goksad Erdagi
Nemanja Dobrota
Slavica Gavric
Aleksandar Stevanovic
Citations