This study evaluates field asphalt aging based on material property changes in pavement with time, and investigates if such changes could have an impact on field rutting performance. Four projects from three different climate zones were monitored as part of the NCHRP 9–49A project at two stages: during pavement construction and two to three years after opening it to traffic. Construction information were collected, and field cores were drilled at both stages to evaluate the material properties of recovered asphalt binder and asphalt mixture. Field rut depth was also measured. In addition, pavement structure, climate and base/subgrade modulus information were also obtained. Results indicate that the asphalt mixture stiffening is caused in major part by asphalt aging. However, the effect of asphalt aging on pavement mixture property may not follow a proportional liner trend. The parameters that are most sensitive to field ageing are MSCR R3.2 and dynamic modulus. It is also found that the variables which showed a good ranking trend with the field rut depth are climate condition (relative humidity, high temperature hour, solar radiation), material properties (Hamburg rut depth, rutting resistance index, high temperature performance grade, MSCR, and dynamic modulus, base and subgrade moduli), as well as air voids.
Electric vehicles (EVs) are one of the most promising applications that are reshaping the future urban mobility market and benefitting the urban environment. Analyzing the adoption of EVs helps both vehicle sales market management and urban transportation-related environmental cost estimation. Previous studies have shown that EV adoption is mostly affected by the economic environment and users’ psychological factors; however, both factors vary among specific urban transportation networks. This paper thus proposes network-related vehicle operating cost functions and a logit-based choice model, which considers both the economic environment and users’ psychological factors at a network level. The model can thus estimate the vehicle adoption for specific networks. Numerical experiments and sensitivity analyses were conducted to illustrate the proposed method and provide practical insights in estimating EV adoption, respectively. The results suggest that EV adoption greatly varies among different cities.
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