Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

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@Article{alshayeb:2021:Energies,

• author = "Suhaib Alshayeb and Aleksandar Stevanovic and B. Brian Park",
• title = "{Field-Based} Prediction Models for Stop Penalty in Traffic Signal Timing Optimization",
• journal = "Energies",
• year = "2021",
• volume = "14",
• number = "21",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1996-1073",
• URL = "https://www.mdpi.com/1996-1073/14/21/7431",
• DOI = "doi:10.3390/en14217431",
• 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.",
• notes = "also known as \cite{en14217431}",

}

Genetic Programming entries for Suhaib Alshayeb Aleksandar Stevanovic B Brian Park

Citations