Dielectric constant is an important parameter for the nondestructive test of cement stabilized macadam base (CSMB) on road by ground-penetrating radar (GPR). However, few studies have been reported on the quantitative relationship between the dielectric constant and the compaction degree, strength indicators, and influencing factors of CSMB. To address the problem, groups of CSMB specimens, which were different in gradation of aggregate (fine or coarse), compaction degree, and curing time, were made and tested for dielectric constant and influencing factors with the help of the Swedish MALA GPR. The relationship between the dielectric constant of CSMB and the influencing factors such as the compaction degree, moisture content, percent residues of aggregate on the sieve of maximum particle size and curing age, and the relationship between the dielectric constant and the unconfined compressive strength were investigated based on several test data and theoretical analysis. The major findings are as follows. There is a good logarithmic correlation between the dielectric constant and the compaction degree of CSMB, and quantitative functions have been established. There is a good linear relationship between the dielectric constant and the unconfined compressive strength of CSMB, and quantitative functions have been established. A comprehensive equation between the dielectric constant of CSMB and the influencing factors such as the compaction degree, moisture content, percent residues of aggregate on the sieve of maximum particle size, and curing age has been established and validated with high significance and small error. The findings are a theoretical basis for the application of GPR to the test and quality assessment of CSMB on roads.

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The key correlating traffic variable for modeling vehicle emissions has evolved from average speed to vehicle-specific power (VSP), and recently to operating mode as defined in Motor Vehicle Emission Simulator (MOVES). The analysis of operating mode and its distribution, however, requires a large amount of data and is time consuming and challenging. This paper attempts to build models between the operating mode distributions and the common traffic variable—average speed—to facilitate the emission estimation. Focusing on light-duty vehicles and unrestricted access roadways, a floating car survey was conducted separately on arterials and collectors in Shaoshan, China. The trajectory data were processed to reveal the characteristics of operating mode distributions. A key finding is that, when the data points of the operating mode of idle are excluded, the VSP distributions of the remaining data points follow logistic distributions and the parameters can be linearly regressed with the average speed. Arterials and collectors feature different operating mode distributions even at the same average speed, and therefore different models were developed. The models were then applied to generate operating mode distributions, which were validated with the real-world data from the test bed and which, when compared with the default values generated by MOVES, fit the real-world condition better.