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

Suhaib Alshayeb, Aleksandar Stevanovic and B. Brian Park
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Suhaib Alshayeb: Department of Civil & Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, 341A Benedum Hall, 3700 O’Hara Street Pittsburgh, Pittsburgh, PA 15261, USA
Aleksandar Stevanovic: Department of Civil & Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, 218D Benedum Hall, 3700 O’Hara Street Pittsburgh, Pittsburgh, PA 15261, USA
B. Brian Park: Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22911, USA

Energies, 2021, vol. 14, issue 21, 1-23

Abstract: 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 “ 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 utilized 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 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 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 optimizing signals to reduce FC.

Keywords: fuel consumption; stops; signalized intersection; stop penalty; performance index; signal timings optimization; multigene genetic programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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