Pavement roughness is one of the key contributors to rolling resistance and thus vehicle fuel consumption. Roughness-induced fuel consumption is the result of energy dissipation in the suspension system of vehicles and therefore depends on both road surface characteristics and vehicle dynamic properties. In this paper, the sensitivity of roughness-induced excess fuel consumption to all involving factors, i.e., road roughness metrics, vehicle dynamic properties, and speed is investigated, and the dominant factors affecting fuel consumption are identified. This is achieved by using the Sobol’s method—a robust analysis of variance (ANOVA)-based technique for global sensitivity analysis. To this end, Monte-Carlo (MC) simulation is performed by generating realizations of all input parameters according to their probability distributions and estimating the energy consumption via a mechanistic roughness model. The results of the simulation are then used to obtain global Sobol sensitivity indices. Finally, the comparison between the Sobol sensitivity indices and the previously employed indices based on Spearman Rank Correlation Coefficient (SRCC) is illustrated. It is found that roughness metrics, i.e. the International Roughness Index (IRI) and the waviness number, account for 88–93% of the total variations in energy dissipation and are the most influential factors affecting the excess fuel consumption. It is also observed that among vehicle dynamic properties, the stiffness of tire is the most important parameter accounting for 2–7% of the total variance of the excess energy consumption.

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We propose, calibrate, and validate a crowdsourced approach for estimating power spectral density (PSD) of road roughness based on an inverse analysis of vertical acceleration measured by a smartphone mounted in an unknown position in a vehicle. Built upon random vibration analysis of a half-car mechanistic model of roughness-induced pavement–vehicle interaction, the inverse analysis employs an L2 norm regularization to estimate ride quality metrics, such as the widely used International Roughness Index, from the acceleration PSD. Evoking the fluctuation–dissipation theorem of statistical physics, the inverse framework estimates the half-car dynamic vehicle properties and related excess fuel consumption. The method is validated against (a) laser-measured road roughness data for both inner city and highway road conditions and (b) road roughness data for the state of California. We also show that the phone position in the vehicle only marginally affects road roughness predictions, an important condition for crowdsourced capabilities of the proposed approach.

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