Vehicle energy consumption and an environmental impact calculation model for the transportation infrastructure systems

Highlights

• The paper introduces a semi-analytical roughness-speed impact (RSI) model.

• The model is used to calculate excess energy consumption due to roughness of pavements.

• The proposed model offers advantages for easy integration and implementation into pavement LCA applications.

• Potential energy savings from pavement roughness can be up to 7% based on 35-year analysis.

Abstract

The interaction of vehicles and tires with various surface and structural features of roadways impact rolling resistance, hence fuel consumption of vehicles. Efficient interaction of vehicles with the roadway provides new opportunities to reduce transportation systems environmental impacts. A roughness–speed impact (RSI) model was developed to quantify the energy and environmental impacts due to vehicle–pavement interaction. The RSI model accounts for the additional rolling resistance of vehicles resulting from pavement surface properties measured in terms of smoothness as well as vehicle efficiency improvements over time. The model uses vehicle-specific power as a basis to develop the analytical equations for representing vehicle–pavement interaction. The proposed analytical model offers advantages for easy integration to LCA software to evaluate GHG reduction policies. According to the model, one unit change of IRI (63.36 in/mile) results in an average increase in fuel consumption of 3% and 2%, respectively, at high and low speeds (65 and 35 mph) for passenger cars. Heavier trucks are less sensitive to IRI change; and one unit change in IRI on average results in 2% and 1% increase, respectively, in fuel consumption of heavy truck at high and low speeds. This paper presents case studies to explore effectiveness of a short-term policy goal concerning with the management of roadway surface features. According to the LCA results with different scenarios of pavement management policies, while vehicle efficiency accounts for about 27% of the potential total energy savings, potential savings from pavement roughness can be up to 7%.

Introduction

The transportation sector accounts for 26% of total U.S. greenhouse emissions (GHG) as of 2014, making it the second largest contributor after the electricity sector (EPA, 2013). Energy and emissions regulations have been proposed by various governing bodies in the U.S. to address long-term energy consumption and emission-reduction goals. One of the recent national fuel-economy regulations was recently developed jointly by the Environmental Protection Agency (EPA) and the National Highway Traffic Safety Administration (NHTSA). The program is projected to cut 6 billion metric tons of GHG over the lifetime of vehicles sold in model years 2012–2025 (EPA, 2012). Such programs have medium-to long-term goals for reducing energy need and GHG through new innovative technologies or technology improvements. Alternatively, pavements can provide opportunities to reduce the GHG emissions as a short-term policy. Various surface characteristics of pavements interacting with tires have impact on the rolling resistance; hence, the fuel consumption of vehicles (Harvey et al., 2016).

Environmental burdens from the transportation system mainly come from the interaction between vehicles and the road, where the tire meets the pavement. Pavement–vehicle interaction (PVI) defines the mechanical and thermal exchange between the bodies—involving vehicles, tires, and pavements and affecting overall rolling resistance of the vehicles. According to a recent report, 32% of the U.S. major roadway system is in poor or mediocre condition, and 42% of major urban highways is congested, costing the nation $101 billion annually in wasted time, fuel, and environmental burdens (Herrmann, 2013). The strategies and programs with the main focus on improving pavement–vehicle interaction to reduce rolling resistance can result in short-term achievements. Programmatic management of pavement-smoothness conditions, improving construction quality, adaptive selection of materials and designs are some of the short-term solutions that can be applied readily.

Life-cycle assessment (LCA), a common sustainability metric, is usually adopted to quantify the effectiveness and environmental consequences of such policies. A pavement life cycle consists of the material production and acquisition, construction, maintenance and rehabilitation, end-of-life, and use stages. Rolling resistance, a major component of pavement LCA use-stage, is defined in ISO 28580:2009 as the loss of energy, or the energy dissipated, per unit of distance traveled (ISO 28580, 2009). Pavement related rolling resistance (RR) include pavement structure, and surface geometry and characteristics (slope, texture, and roughness) (Bendtsen, 2004, Igwe et al., 2009, Lepert and Brillet, 2009). Fig. 1 shows the pavement-vehicle interaction in a joint pavement-vehicle LCA framework. It has been estimated that about 20% of transportation-related consumption is caused by rolling resistance (IEA, 2005).

Pavement roughness (or lack of smoothness) is generally cited throughout the literature as the pavement characteristic having the major role in rolling resistance and hence the fuel economy of the moving vehicles, and often used by highway agencies as international roughness index (IRI) reflecting overall roadway network health (Sandberg, 1990, Sandberg, 2011, Santero and Horvath, 2009, Zhang et al., 2009, Hammarström et al., 2012, Bryce et al., 2011).

In an effort to quantify fuel consumption due to pavement characteristics, World Bank's HDM-4 (highway development and management model) model was developed based on principles of vehicle-specific power (VSP) (Bennett and Greenwood, 2003). This model requires some of its parameters to be calibrated using raw data or existing regression-based fuel models. Chatti and Zaabar (2012) calibrated the HDM-4 model using five instrumented vehicles at three speeds. The model provides an estimate of additional fuel consumption for several types of vehicles as a function of increasing IRI at discrete speeds where the field experiments were conducted. Louhghalam et al. (2017) implemented HDM4 model along with a pavement-deflection related RR model to illustrate the extent of pavement structural and surface characteristics in managing the road network. Other field tests with different vehicles and test conditions have also been performed (Ejsmont et al., 2012, Amos, 2006, Bienvenu and Jiao, 2013). However, a comprehensive field-test matrix was not feasible because many other relevant parameters affecting the rolling resistance are almost impossible to control (Andersen et al., 2014, Kim et al., 2016). Therefore, there are limited field observations; and the existing ones are not sufficient to develop a generalized model estimating rolling resistance for different vehicles due to pavement roughness and vehicle operating conditions (AzariJafari et al., 2016). Current models are generally empirical, accounts for only discrete vehicle speeds and limited to only energy consumption.

Alternatively, simulation methods like EPA's MOVES (motor vehicle emission simulator) (EPA, 2015) can be used to evaluate moving vehicles' environmental impacts. MOVES is an open-source software program used to estimate energy consumption and emissions for moving vehicles as a function of vehicle type, age, technology, fuel type, environment, road grade, etc. Although MOVES can estimate vehicle emissions by considering a wide range of factors, the current version does not consider the roadway surface conditions on vehicles' fuel consumption and emissions. Modification of various coefficients in MOVES was needed to reflect road surface characteristics (Wang et al., 2012, Ghosh et al., 2015). In addition, it usually requires time-consuming simulations prohibiting the use of MOVES directly with LCA software.

Currently, there is no generalized analytical formulations or expressions to relate IRI to environmental impacts. Therefore, an external software is commonly used for comprehensive pavement LCAs in which use stage is the focus at the project level (Wang et al., 2012). Although EPA's MOVES program in conjunction with the HDM-4 model can be used to calculate project-specific life-cycle impacts, there is no simplified method to calculate environmental impacts from moving vehicles such as used in project level pavement LCA. Such a simplified yet sufficiently accurate method will also be needed for network level calculations that can be used to estimate contribution of infrastructure condition to transportation related GHG emissions. Therefore, this paper proposes a simple to use yet consistent and accurate rolling resistance model to use in pavement LCA applications.

This paper builds upon previous studies and introduces generalized formulations stemming from vehicle-specific power models to estimate rolling resistance and fuel consumption resulting from the changes in the roughness profile of roadways and vehicle operating conditions. In order to be able to make future projections, the model also accounts for vehicle-efficiency increases over time. The proposed model specifically targets the following gaps in the literature: First, to develop formulations to relate vehicle energy consumption to pavement roughness while considering a wide range of vehicle types and speeds. Second, to consider vehicle efficiency improvements over time. Then, finally to extend the models to other environmental indicators other than GHG such as acidification, eutrophication, ecotoxicity, carcinogenics, etc. This set of impact indicators are included in most LCAs to provide a multi-point environmental assessment of a product or service, rather than relying on only GWP. Finally, all these upgrades were framed into formulations that can be either used directly to estimate overall vehicle consumption or in incremental way to estimate only pavement roughness effect. The proposed model is an easy implementable method for LCA tool and software development purposes.