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An elastoplastic constitutive model that takes into account the stress–strain relationship and creep-induced hardening behavior of rockfill materials is proposed in light of previous experimental observations. It is assumed that the mechanical response during loading and the final amounts of creep strains under a constant stress state are independent of the strain rate. The focus of the proposed model is the coupling effect between loading and creep, including the influence of loading history on subsequent creep strains and the influence of creep history on subsequent loading behavior. An extended yield function, which allows flexible control over the shape of yield surfaces, is used not only to distinguish among loading, unloading, and neutral loading, but also to manipulate the creep-induced hardening using a plastic strains–based hardening parameter. A stress-dependent dilatancy equation is used, instead of a plastic potential function, to define the directions of plastic strains during loading. The hardening law is established based on three different types of experimental results. Only routine experiments are required for calibration of model parameters, and the model can be used in a reduced form according to the available test results. The model is verified using typical experimental data and is found to be capable of capturing important behavior of rockfill materials, such as pressure-dependent strength, shear contraction and dilation, and creep-induced stiffening.

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The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and date attributes on AS prediction has been analyzed. Then, through spatiotemporal correlations calculation based on the data from Global Positioning System (GPS), the predictive method utilizes the recursive least squares method to fuse the historical AS with other factors (such as weather, date attributes, etc.) and adopts an extended Kalman filter algorithm to accurately predict the AS of the target segment. Finally, we applied our approach on the traffic congestion prediction on four road segments in Chengdu, China. The results showed that the proposed predictive model is highly feasible and accurate.