RolRoad – LCA: A Web-Based Application for Estimating the Excess Fuel Consumption and Environmental Impacts Due to the Rolling Resistance of Passenger Cars

This paper presents RolRoad – LCA, a web-based application for estimating rolling resistance (RR) and its effect on excess fuel consumption (EFC) of passenger cars driving on flexible road pavement. RolRoad – LCA relies on machine learning models developed based on data collected with an instrumented vehicle at the University of Gustave Eiffel’s test track. It is intended for project-level analysis and considers the effects of roughness, expressed in terms of the international roughness index (IRI), and macrotexture, expressed in terms of mean profile depth (MPD), on RR and EFC. Further, it allows the execution of three types of analysis: single-year, multiple-year considering linear degradation of pavement surface properties, and multiple-year considering non-linear degradation. The life cycle inventory associated with EFC is calculated according to the dataset corresponding to the operation of a standard passenger car powered by gasoline with an engine complying with the EURO 5 standard. The resulting environmental impacts are expressed in an environmental cost indicator (MKI), which is a single-score indicator expressed in Euros, that aggregates different midpoint impact category scores. RolRoad – LCA offers the pavement community an expedient and accessible application to quantify and compare the effects of different pavement surface properties and maintenance and rehabilitation (M&R) strategies on the EFC and corresponding environmental impacts. It can be used as a standalone tool or complement the use of generic or pavement-specific life cycle assessment (LCA) software.