Connected vehicles present an opportunity to monitor pavement condition continuously by analyzing data from vehicle-integrated position sensors and accelerometers. The current practice of characterizing and reporting ride-quality is to compute the international roughness index (IRI) from elevation profile or bumpiness measurements. However, the IRI is defined only for a reference speed of 80 kilometers per hour. Furthermore, the relatively high cost for calibrated instruments and specialized expertise needed to produce the IRI limit its potential for widespread use in a connected vehicle environment. This research introduces the road impact factor (RIF) which is derived from vehicle integrated accelerometer data. The analysis demonstrates that RIF and IRI are directly proportional. Simultaneous data collection with a laser-based inertial profiler validates this relationship. A linear combination of the RIF from different speed bands produces a time-wavelength-intensity-transform (TWIT) that, unlike the IRI, is wavelength-unbiased. Consequently, the TWIT enables low-cost, network-wide and repeatable performance measures at any speed. It can extend models that currently use IRI data by calibrating them with a constant of proportionality.

This paper highlights the importance of energy harvesting in high-value asset monitoring applications involving use of active RFID tags. The paper begins by highlighting advantages of active tags including improved range and read rate in electromagnetically unfriendly environments. Although a battery can substantially improve performance, it limits maintenance-free operational life. Therefore, harvesting energy from sources such as vibration is shown to address this shortcoming but these sources must be adequate, available throughout the life of the application, and highly efficient. Piezoelectric vibration energy harvesting design procedures and components for such systems are identified. This includes three key components namely, the energy harvesting transducer, power management circuit, and energy storage device. Each component of the energy harvesting system is described and important design criteria are highlighted. Finally, the paper concludes by analyzing vibration data from high value assets used during disaster relief, and describing preliminary results of an energy harvesting prototype with details on system form factors, efficiency, and life.

Transportation agencies rely on the accurate localization and reporting of roadway anomalies that could pose serious hazards to the traveling public. However, the cost and technical limitations of present methods prevent their scaling to all roadways. Connected vehicles with on-board accelerometers and conventional geospatial position receivers offer an attractive alternative because of their potential to monitor all roadways in real-time. The conventional global positioning system is ubiquitous and essentially free to use but it produces impractically large position errors. This study evaluated the improvement in precision achievable by augmenting the conventional geo-fence system with a standard speed bump or an existing anomaly at a pre-determined position to establish a reference inertial marker. The speed sensor subsequently generates position tags for the remaining inertial samples by computing their path distances relative to the reference position. The error model and a case study using smartphones to emulate connected vehicles revealed that the precision in localization improves from tens of metres to sub-centimetre levels, and the accuracy of measuring localized roughness more than doubles. The research results demonstrate that transportation agencies will benefit from using the connected vehicle method to achieve precision and accuracy levels that are comparable to existing laser-based inertial profilers.

The Road Impact Factor is a measure of ride-quality. It is derived from the average inertial response of vehicles to road roughness. Unlike the International Roughness Index, the most common measure, the road impact factor does not rely on specialized instrumentation to measure spatial deviations from a flat profile. The most significant advantage of the Road Impact Factor is that low-cost sensors distributed in smartphones and connected vehicles generate the measurements directly. Standardizing the sample rate of inertial sensors in vehicles will provide consistent measures at any speed. This study characterizes the impact of sample rate and traversal volume on measurement consistency, and conducts case studies to validate the theories developed for a recommended standard at 64 hertz.