Introduced autonomous and connected vehicles equipped with emerging technologies are expected to change the automotive market. In this study, using stated preference (SP) data collected from choice experiments conducted in Korea with a mixed multiple discrete-continuous extreme value model (MDCEV), we analyzed how the advent of next-generation of vehicles with advanced vehicle technologies would affect consumer vehicle choices and usage patterns. Additionally, ex-ante market simulations and market segmentation analyses were conducted to provide specific management strategies for next-generation vehicles. The results showed that consumer preference structures of conventional and alternative fuel types primarily differed depending on whether they were drivers or non-drivers. Additionally, although the introduction of electric vehicles to the automobile market is expected to negatively affect the choice probability and mileage of other vehicles, it could have a positive influence on the probability of purchasing an existing conventional vehicle if advanced vehicle technologies are available.
The pavement structural number (SN) is used in various applications worldwide. One application is the structural condition index. The structural condition index was developed for the Texas Department of Transportation (DOT) to support maintenance and rehabilitation decisions at the network level and is calculated as the ratio of two SN values (effective SN/required SN). A direct method of determining the SN from falling weight deflectometer (FWD) testing has been used to evaluate the effective SN. This study sought to increase the accuracy of the structural condition index by improving the method of determining the effective SN from FWD deflection data. A new equation was developed by modifying the equation that was previously used, and new coefficients for four different flexible pavement types were used. The new equation was improved by the addition of new variables for considering the depth to a rigid layer and by the use of a large database consisting of hypothetical pavement structures and responses. This effort not only benefits the Texas DOT by allowing increased accuracy in implementing the structural condition index but also helps other agencies improve their evaluation of the SN by FWD deflection at the network level. In this study, only surface-treated pavement and asphalt concrete pavement were evaluated; portland cement concrete pavement was not addressed.
The morphology of aggregate particles used for pavement construction plays an essential role in the structural capacity and safety performance of pavement structures. Each of the three main components of aggregate morphology (form, angularity, and texture) has a distinct effect on pavement performance and corresponds to a different frequency range. Considering the challenge in segregating form, angularity, and texture in the space domain, characterizing them separately in the frequency domain would be beneficial and would allow for a more objective and detailed classification system for aggregate morphology. This study focuses on the characterization of aggregate angularity in the frequency domain with the objective of obtaining a parameter that is free of individual subjectivity. Since aggregate angularity is a subjective visual descriptor of aggregate shape variations at corners, a survey was conducted of pavement engineers to collect visual ratings of aggregate angularities using a set of aggregates. Thereafter, using the average visual ratings from the survey responses as reference, three common aggregate angularity indexes were evaluated: roundness, the University of Illinois Aggregate Image Analyzer (UIAIA) angularity index, and the Aggregate Image Measurement System (AIMS) angularity index. In addition, with the aid of the discrete Fourier transform (DFT) algorithm, the contributing frequencies were acquired for visual rating, along with roundness and the UIAIA and AIMS angularity indexes. Based on the contributing frequencies identified, prediction models were successfully established for visual rating: roundness and the UIAIA and AIMS angularity indexes. It was concluded that DFT can be accurate in objectively assessing angularity and that roundness is the more robust parameter and can be accurately predicted by the models developed.
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