Traffic simulation models have seen increasing use during the past decades. One of the biggest challenges related to their successful application is effective calibration and validation. Emerging data collection techniques provide richer data that can be used to improve this process. In this research, we explore the use of distributions of collected data (such as accelerations, using opportunistic sensors, such as smart-phone accelerometers) for calibration purposes. The performance of the considered ubiquitous sensors is benchmarked against reference equipment, to evaluate its accuracy under different conditions. A methodology is proposed for the integration of distributions of data in traffic simulation model calibration and validation.
This paper presents the dynamic testing of a roadway, single-span, cable-stayed bridge for a sequence of static load and ambient vibration monitoring scenarios. Deck movements were captured along both sideways of the bridge using a Digital Image Correlation (DIC) and a Ground-based Microwave Interfererometer (GBMI) system. Cable vibrations were measured at a single point location on each of the six cables using the GBMI technique.Dynamic testing involves three types of analyses; firstly, vibration analysis and modal parameter estimation (i. e., natural frequencies and modal shapes) of the deck using the combined DIC and GBMI measurements. Secondly, dynamic testing of the cables is performed through vibration analysis and experimental computation of their tension forces. Thirdly, the mechanism of cable-deck dynamic interaction is studied through their Power Spectra Density (PSD) and the Short Time Fourier Transform (STFT) analyses. Thereby, the global (deck and cable) and local (either deck or cable) bridge modes are identified, serving a concrete benchmark of the current state of the bridge for studying the evolution of its structural performance in the future. The level of synergy and complementarity between the GBMI and DIC techniques for bridge monitoring is also examined and assessed.
Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, cooperative positioning (CP) solutions have attracted attention in order to enhance the accuracy and reliability of low-cost GNSS receivers, especially in complex propagation environments. In this paper, the problem of efficient and robust CP employing low-cost GNSS receivers is investigated over critical ITS scenarios. By adopting a Cooperative-Differential GNSS (C-DGNSS) framework, the target’s vehicle receiver can obtain Position–Velocity–Time (PVT) corrections from a neighboring vehicle and update its own position in real-time. A ranking module based on multi-attribute decision-making (MADM) algorithms is proposed for the neighboring vehicle rating and optimal selection. The considered MADM techniques are simulated with various weightings, normalization techniques, and criteria associated with positioning accuracy and reliability. The obtained criteria values are experimental GNSS measurements from several low-cost receivers. A comparative and sensitivity analysis are provided by evaluating the MADM algorithms in terms of ranking performance and robustness. The positioning data time series and the numerical results are then presented, and comments are made. Scoring-based and distance-based MADM methods perform better, while L1 RMS, HDOP, and Hz std are the most critical criteria. The multi-purpose applicability of the proposed scheme, not only for land vehicles, is also discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.