Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization
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- @Article{alshayeb:2021:Energies,
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author = "Suhaib Alshayeb and Aleksandar Stevanovic and
B. Brian Park",
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title = "{Field-Based} Prediction Models for Stop Penalty in
Traffic Signal Timing Optimization",
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journal = "Energies",
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year = "2021",
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volume = "14",
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number = "21",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1996-1073",
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URL = "https://www.mdpi.com/1996-1073/14/21/7431",
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DOI = "doi:10.3390/en14217431",
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abstract = "Transportation agencies optimise signals to improve
safety, mobility, and the environment. One commonly
used objective function to optimise signals is the
Performance Index (PI), a linear combination of delays
and stops that can be balanced to minimise fuel
consumption (FC). The critical component of the PI is
the stop penalty K, which expresses an FC stop
equivalency estimated in seconds of pure delay. This
study applies vehicular trajectory and FC data
collected in the field, for a large fleet of modern
vehicles, to compute the K-factor. The tested vehicles
were classified into seven homogenous groups by using
the k-prototype algorithm. Furthermore, multigene
genetic programming (MGGP) is used to develop
prediction models for the K-factor. The proposed
K-factor models are expressed as functions of various
parameters that impact its value, including vehicle
type, cruising speed, road gradient, driving behaviour,
idling FC, and the deceleration duration. A parametric
analysis is carried out to check the developed models'
quality in capturing the individual impact of the
included parameters on the K-factor. The developed
models showed an excellent performance in estimating
the K-factor under multiple conditions. Future research
shall evaluate the findings by using field-based
K-values in optimising signals to reduce FC.",
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notes = "also known as \cite{en14217431}",
- }
Genetic Programming entries for
Suhaib Alshayeb
Aleksandar Stevanovic
B Brian Park
Citations