This paper details the development of a new 1‑D combined finite difference and finite element procedure for calculating in-depth pavement temperatures, which has been implemented in the CalME design method.  The model is driven by a database of known surface temperatures rather than raw climatic inputs and an energy balance at the surface, since it was noted that the pavement structure had little impact on the surface temperature (if the surface properties remain constant).  The model runs quickly, enabling direct simulation of in-depth temperatures while performing Monte Carlo based simulation of pavement reliability.  The disadvantages to this approach are that it requires the surface temperatures to be developed independently and that it does not have a coupled moisture model for the prediction of freeze/thaw conditions.  The paper also details some anomalies in the output of the Enhance Integrated Climatic Model (used in the new AASHTO mechanistic-empirical design method), that were found during development.  Finally, the paper collects various published values for thermal properties of pavement materials, to aid in the implementation of thermal models.

Weigh-in-motion (WIM) devices measure and record highway vehicle axle loads. The data they collect include axle loads and spacing, vehicle classification and gross weights, and travel speed. These WIM data are used for pavement design, management, and performance studies. In 2016, there were 123 WIM devices operating on sites throughout the California state-owned highway network, one of the densest and best maintained in the United States. The University of California Pavement Research Center (UCPRC) has studied California’s WIM data with the California Department of Transportation (Caltrans) since 2007 to advance mechanistic-empirical (ME) pavement design procedures. Previously, the UCPRC completed the first analysis of the WIM data collected from 1998 to 2003 to discover similarities in axle load distributions at the WIM sites and then grouped them using cluster analysis to generate default traffic inputs for pavement design software. For this research, the UCPRC processed WIM data collected from 80 California WIM-measuring sites from 2004 to 2015, identified the axle load distribution for each site, and updated the earlier grouping using hierarchical cluster analysis. A decision tree was developed to classify WIM data into five WIM axle load spectra, which have been implemented in the Caltrans pavement management system database, PaveM, to generate truck traffic inputs in the CalME and Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement design software tools used by Caltrans for asphalt and concrete pavement design, respectively.

The results presented in this report are part of Phase II of a two-phase study. Based on the results from mechanistic models of additional fuel consumption in vehicles due to the structural response of the pavement structure, Phase I of this study concluded that pavement has a small but important enough effect on vehicle fuel consumption to warrant field investigation. The goal of the Phase II study was to measure vehicle fuel consumption in the field on different pavement types in winter and summer and at different speeds, and to use the data collected to develop empirical models for this fuel-consumption effect. The field investigation presented in this report included 21 California pavement sections with different pavement types: flexible, semi-rigid, jointed plain concrete, continuously reinforced concrete, and composite structures. The vehicles selected and instrumented for the fuel economy measurements included a five-axle semi-trailer tractor, a diesel truck, a sports utility vehicle (SUV), a gasoline-fueled car, and a diesel-fueled car. Vehicles were run on cruise control and data were recorded at 45 and 55 mph on state roads and at 35 and 45 mph on local roads. The data from the field investigation were analyzed and used to develop an empirical modeling framework considering road geometry, wind, temperature, and pavement structural and surface (roughness and texture) effects on vehicle fuel consumption. Based on the final framework, a final empirical model was developed for each section. The report presents results of a factorial analysis of the effects of each variable using the final model for each vehicle type on each pavement type and in different California climate regions. The within-section variability is almost always greater than the variability between sections for a given pavement type and efficiency condition (tailwind, speed, and climate region) and the within-section variability is also usually larger than the variability between pavement types. Only the data for the heavy heavy-duty truck (HHDT) showed any meaningful difference in results between sections, but that variability is not tied to pavement type and is only present under certain conditions of speed, tailwind, and air temperature (tied to climate region). These results indicate that missing variables (or errors in the existing variables) need to be reduced in further experiments to observe measurable effects of pavements on fuel consumption in real-world driving. While air temperature interacted with cruise control speed for the HHDT, there was a lack of clear evidence that asphalt roads cause more fuel consumption for the HHDT even under the conditions where the most possible effect of pavement type was found. This suggests that pavement type is not the correct explanation for that variation. Instead, the variation in the effect of air temperature by cruise control speed for the HHDT likely has to do with differences in engine efficiency under different conditions.

An Environmental Justice (EJ) analysis was carried out using full Chemical Transport Models (CTMs) over Los Angeles, California, to determine how the combination of domain size and spatial resolution affects predicted air pollution disparities in present day and future simulations when data support from measurements is not available. One set of simulations used the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF/Chem) with spatial resolution ranging from 250 m to 36 km, comparable to census tract sizes, over domains ranging in size from 320 km² to 10,000 km². A second set of simulations used the UCD/CIT CTM with spatial resolution ranging from 4 km to 24 km over domains ranging in size from 98,000 km² to 1,000,000 km². Overall WRF/Chem model accuracy improved approximately 9% as spatial resolution increased from 4 km to 250 m in present-day simulations, with similar results expected for future simulations. Exposure disparity results are consistent with previous findings: the average Non-Hispanic White person in the study domain experiences PM_2.5 mass concentrations 6-14% lower than the average resident, while the average Black and African American person experiences PM_2.5 mass concentrations that are 3-22% higher than the average resident. Predicted exposure disparities were a function of the model configuration. Increasing the spatial resolution finer than approximately 1 km produced diminishing returns because the increased spatial resolution came at the expense of reduced domain size in order to maintain reasonable computational burden. Increasing domain size to capture regional trends, such as wealthier populations living in coastal areas, identified larger exposure disparities but the benefits were limited. CTM configurations that use spatial resolution/domain size of 1 km/10³ km² and 4 km/10⁴ km² over Los Angeles can detect a 0.5 μg m^-3 exposure difference with statistical power greater than 90%. These configurations represent a balanced approach between statistical power, sensitivity across socio-economic groups, and computational burden when predicting current and future air pollution exposure disparities in Los Angeles.