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Speed Estimation from Single Loop Data Using an Unscented Particle Filter

Computer-Aided Civil and Infrastructure Engineering (Wiley InterScience) - Fri, 01/10/2010 - 1:00am

Abstract: This article presents a hybrid method, the Unscented Particle Filter (UPF), for traffic flow speed estimation using single loop outputs. The Kalman filters used in past speed estimation studies employ a Gaussian assumption that is hardly satisfied. The hybrid method that combines a parametric filter (Unscented Kalman Filter) and a nonparametric filter (Particle Filter) is thus proposed to overcome the limitations of the existing methods. To illustrate the advantage of the proposed approach, two data sets collected from field detectors along with a simulated data set are utilized for performance evaluation and comparison with the Extended Kalman Filter and the Unscented Kalman Filter. It is found that the proposed method outperforms the evaluated Kalman filter methods. The UPF method produces accurate speed estimation even for congested flow conditions in which many other methods have significant accuracy problems.

Categories: Computing in Civil Engineering (Journal)

Mobile Agent Computing Paradigm for Building a Flexible Structural Health Monitoring Sensor Network

Computer-Aided Civil and Infrastructure Engineering (Wiley InterScience) - Fri, 01/10/2010 - 1:00am

Abstract: Wireless structural health monitoring research has drawn great attention in recent years from various research groups. While sensor network approach is a feasible solution for structural health monitoring, the design of wireless sensor networks presents a number of challenges, such as adaptability and the limited communication bandwidth. To address these challenges, we explore the mobile agent approach to enhance the flexibility and reduce raw data transmission in wireless structural health monitoring sensor networks. An integrated wireless sensor network consisting of a mobile agent-based network middleware and distributed high computational power sensor nodes is developed. These embedded computer-based high computational power sensor nodes include Linux operating system, integrate with open source numerical libraries, and connect to multimodality sensors to support both active and passive sensing. The mobile agent middleware is built on a mobile agent system called Mobile-C. The mobile agent middleware allows a sensor network moving computational programs to the data source. With mobile agent middleware, a sensor network is able to adopt newly developed diagnosis algorithms and make adjustment in response to operational or task changes. The presented mobile agent approach has been validated for structural damage diagnosis using a scaled steel bridge.

Categories: Computing in Civil Engineering (Journal)

Wavelet-Based Denoising for Traffic Volume Time Series Forecasting with Self-Organizing Neural Networks

Computer-Aided Civil and Infrastructure Engineering (Wiley InterScience) - Fri, 01/10/2010 - 1:00am

Abstract: In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near-term traffic volumes to feed real-time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short-term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet-based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self-organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real-world data sets from three interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by four other relevant prediction models shows a favorable outcome.

Categories: Computing in Civil Engineering (Journal)

Automatic Detection of Deficient Video Log Images Using a Histogram Equity Index and an Adaptive Gaussian Mixture Model

Computer-Aided Civil and Infrastructure Engineering (Wiley InterScience) - Fri, 01/10/2010 - 1:00am

Abstract: Video log images are often used by transportation agencies to manually or automatically extract roadway infrastructure information, including roadway geometry, signs, etc. Poor-quality images, especially those having illumination-related deficiencies caused by color corruption with a plain-like grayscale histogram, sun glare, or darkness problems, are unacceptable and need to be identified. Manually reviewing the tens of millions of video log images for quality control is labor intensive and time-consuming, so there is a need to develop automatic video log image quality control procedures. The contribution of this article is that it formulates a new problem of roadway video log image quality control and then proposes a reasonable solution to address this problem in the hope that it will motivate the development of new algorithms by other researchers. For the first time, an algorithm using a Histogram Equity Index (HEI) and an adaptive Gaussian Mixture Model is proposed to address the video log image quality issue by automatically detecting illumination-related deficiencies. The Alberta Department of Transportation provided 15,489 video log images to test the proposed algorithm. Test results show that the developed algorithm can detect illumination-related video log image deficiencies with a false positive rate of 4%, 3%, and 12%; a false negative rate of 15%, 17%, and 19% for plain-like color corruption, dark, and sun glare conditions, respectively; computation time is 0.1 second/image. The proposed algorithm could potentially be used to improve video log image data quality control.

Categories: Computing in Civil Engineering (Journal)

Exploring the Deterioration Factors of RC Bridge Decks: A Rough Set Approach

Computer-Aided Civil and Infrastructure Engineering (Wiley InterScience) - Fri, 01/10/2010 - 1:00am

Abstract: Information about the factors that lead to the deterioration of bridges is essential for bridge maintenance. Pinpointing what these factors are will certainly enhance the effectiveness of bridge management. However, a review of the literature reveals that such factors are mainly determined based on experts’ opinions rather than a systematic approach. In this study the factors leading to deterioration of RC bridge decks are grouped into six common types. Twenty-nine candidate factors are selected from an extensive review of past work as well as from the inventory of the Taiwan Bridge Management System. A data mining technique, the Rough Set Theory (RST), is employed to find the factors that have the most significant impact on deterioration. It is found that weather-related factors are rather significant for almost all types of deterioration. Finally, the factors mined by RST are compared to those obtained by Mann-Whitney U (MWU). The results of comparison appear fairly consistent, which validates the proposed approach.

Categories: Computing in Civil Engineering (Journal)

Rotational Speed Sensors for Various Applications

Sensor Industry News | sensorportal.com - Thu, 26/08/2010 - 2:20am

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