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

Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component o...

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Autores principales:	Suhaib Alshayeb, Aleksandar Stevanovic, B. Brian Park
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fuel consumption stops signalized intersection stop penalty performance index signal timings optimization Technology T
Acceso en línea:	https://doaj.org/article/3285246d037f414da0b1078c103d7023
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spelling	oai:doaj.org-article:3285246d037f414da0b1078c103d70232021-11-11T16:08:38ZField-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization10.3390/en142174311996-1073https://doaj.org/article/3285246d037f414da0b1078c103d70232021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7431https://doaj.org/toc/1996-1073Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component of the PI is the stop penalty “<i>K</i>”, 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 <i>K</i>-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is utilized to develop prediction models for the <i>K</i>-factor. The proposed <i>K</i>-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behavior, 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 <i>K</i>-factor. The developed models showed an excellent performance in estimating the <i>K</i>-factor under multiple conditions. Future research shall evaluate the findings by using field-based <i>K</i>-values in optimizing signals to reduce FC.Suhaib AlshayebAleksandar StevanovicB. Brian ParkMDPI AGarticlefuel consumptionstopssignalized intersectionstop penaltyperformance indexsignal timings optimizationTechnologyTENEnergies, Vol 14, Iss 7431, p 7431 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuel consumption stops signalized intersection stop penalty performance index signal timings optimization Technology T
spellingShingle	fuel consumption stops signalized intersection stop penalty performance index signal timings optimization Technology T Suhaib Alshayeb Aleksandar Stevanovic B. Brian Park Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization
description	Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component of the PI is the stop penalty “<i>K</i>”, 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 <i>K</i>-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is utilized to develop prediction models for the <i>K</i>-factor. The proposed <i>K</i>-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behavior, 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 <i>K</i>-factor. The developed models showed an excellent performance in estimating the <i>K</i>-factor under multiple conditions. Future research shall evaluate the findings by using field-based <i>K</i>-values in optimizing signals to reduce FC.
format	article
author	Suhaib Alshayeb Aleksandar Stevanovic B. Brian Park
author_facet	Suhaib Alshayeb Aleksandar Stevanovic B. Brian Park
author_sort	Suhaib Alshayeb
title	Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization
title_short	Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization
title_full	Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization
title_fullStr	Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization
title_full_unstemmed	Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization
title_sort	field-based prediction models for stop penalty in traffic signal timing optimization
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3285246d037f414da0b1078c103d7023
work_keys_str_mv	AT suhaibalshayeb fieldbasedpredictionmodelsforstoppenaltyintrafficsignaltimingoptimization AT aleksandarstevanovic fieldbasedpredictionmodelsforstoppenaltyintrafficsignaltimingoptimization AT bbrianpark fieldbasedpredictionmodelsforstoppenaltyintrafficsignaltimingoptimization
_version_	1718432405315911680

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

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