Cycle-by-cycle Delay Estimation at Signalized Intersections by using Machine Learning and Simulated Video Detection Data

Created by W.Langdon from gp-bibliography.bib Revision:1.7656

@InProceedings{Erdagi:2023:MT-ITS,

• author = "Ismet Goksad Erdagi and Nemanja Dobrota and Slavica Gavric and Aleksandar Stevanovic",
• booktitle = "2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)",
• title = "Cycle-by-cycle Delay Estimation at Signalized Intersections by using Machine Learning and Simulated Video Detection Data",
• year = "2023",
• 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.",
• keywords = "genetic algorithms, genetic programming, Uncertainty, Estimation, Detectors, Delay estimation, Machine learning, Benchmark testing, performance measures, delay, machine learning, traffic, video detection",
• DOI = "doi:10.1109/MT-ITS56129.2023.10241732",
• month = jun,
• notes = "Also known as \cite{10241732}",

}

Genetic Programming entries for Ismet Goksad Erdagi Nemanja Dobrota Slavica Gavric Aleksandar Stevanovic

Citations