In recent years the pavement engineering community has shown increasing interest in shifting from a stationary Falling Weight Deflectometer (FWD) to moving testing platforms such as the Traffic Speed Deflectometer (TSD). This paper dealt with comparing TSD measurements against FWD measurements; it focused on the comparison methodology, utilizing experimental data for demonstration. To better account for differences in loading conditions between the two devices a new FWD deflection index was formulated first. This index served as reference/benchmark for assessing the corresponding TSD measurements. Next, a Taylor diagram was proposed for visualizing several comparison statistics. Finally, a modern agreement metric was identified and applied for ranking comparison results across different datasets. Overall, the suggested methodology is deemed generic and highly applicable to future situations, especially for assessing the worth of emerging device upgrades or improved interpretation schemes (or both).

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Modern cars are equipped with many sensors that measure information about the vehicle and its surroundings. These measurements are therefore related to the ride-surface conditions over which the vehicle is passing. The paper commences by outlining a four-component vision for performing road condition evaluation based on in-vehicle sensor readings and subsequent feeding of pavement management systems (PMSs) with live condition information. This is expected to enrich the functionalities of PMSs, and ultimately lead to improved maintenance and repair decisions. Next the LiRA (Live Road Assessment) project—an ongoing proof-of-concept attempt to realize the vision components—is presented. The project elements and software architecture are described in detail, listing any assumptions, compromises, and challenges. LiRA involves a fleet of 400 electric cars operating in Copenhagen, both within the city streets and nearby highways. Sensor data collection is performed with a customized Internet of Things (IoT) device installed in the cars. Data processing and structuring involve new software tools for quality control, spatio-temporal interpolation, and map matching. A hybrid approach, combining machine learning models with physical (mechanics-based) models, is currently being applied to convert data into relevant information for PMSs. Based on the experience thus far with LiRA, the vision actualization is deemed achievable, workable, and up-scalable.

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