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.

Highlights

  • Introduction and BackgroundEmissions of greenhouse gases (GHG) are a significant public concern due to their association with the ongoing climate change [1,2]

  • This study investigates the stop penalty by utilizing a dataset provided by the Department of Energy (DOE) [27] and collected by the Idaho National Lab [28]

  • The DOE provided the dataset as a giant Comma-separated values (CSV) file; it was necessary to divide the entire dataset into smaller subsets for easier handling

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Summary

Introduction and Background

Emissions of greenhouse gases (GHG) are a significant public concern due to their association with the ongoing climate change [1,2]. A recent study by Stevanovic et al [23] proposed an analytical model (described later) to compute the K-factor by making a complete distinction between the FC caused by the deceleration-acceleration event (stopping action) and fuel consumed while idling (zero speed). Despite these great efforts, the literature has not estimated the K-factor based on very representative field datasets collected for a large number of various vehicles whose FCs may vary.

Methodology
Overview of the Stop Penalty Derivation
Collection of Field Data
Data Preparation
Vehicle Classification
Instantaneous Fuel Consumption Rates
Cruising Speeds and CSSPs
Road Gradient
Multigene Genetic Programming
Results and Discussion
Parametric Analysis
Comparison of Stop Penalties from Various Studies
Conclusions and Future Research
Full Text
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