Showing papers by "California Department of Transportation published in 2018"
TL;DR: The time-varying spatial random effects model demonstrated the best performance in both site consistency and method consistency, and appeared to have the largest value of deviance information criterion (DIC) in terms of the comparison between models based on site ranking performance.
34 citations
TL;DR: Simulation results indicated that the traffic flow arrival pattern has significant impacts on Ramp queue length; vehicle platoons released from the upstream signalized intersection tend to exacerbate ramp queue length.
Abstract: This paper compares queue lengths at the two different on-ramp configurations: arterial-to-freeway ramp and freeway-to-freeway connector. Mesoscopic queue length simulation models are developed bas...
18 citations
01 Mar 2018
TL;DR: In this article, the required acceleration length of vehicles accelerating from a stopped position was estimated for the case of vehicles with a constant acceleration rate, and a constant rate was assumed for the sake of simplicity.
Abstract: When estimating the required acceleration length of vehicles accelerating from a stopped position, a constant acceleration rate is sometimes assumed for the sake of simplicity. Nevertheless...
10 citations
TL;DR: Numerical experiments indicate that the model can be calibrated to reproduce a fundamental diagram that matches an empirical one proposed by Weidmann, and prove the model to be a useful tool for study of pedestrian dynamics.
Abstract: A pedestrian's physical movement is simulated as a response to the pedestrian subjective evaluation of the objective environment. The objective environment is modeled by presumed fields statically or dynamically superposed. Regulation functions, which consider not only force caused by presumed fields but also local crowd densities around pedestrians, are introduced for consideration of pedestrians' intelligence. Numerical experiments indicate that the model can be calibrated to reproduce a fundamental diagram that matches an empirical one proposed by Weidmann. Such experiments prove the model to be a useful tool for study of pedestrian dynamics.
8 citations
20 Jun 2018
TL;DR: In this article, a two-staged pavement image processing framework is presented, where the pavement images are classified into four general categories in the first stage, so that the images can be processed using category-specific algorithms in the 2nd stage.
Abstract: In this paper, a novel two-staged pavement image processing framework is presented. The pavement images are classified into four general categories in the first stage, so that the images can be processed using category-specific algorithms in the 2nd stage. The proposed algorithm first fuses a local contrast enhanced image with a global grayscale corrected image to obtain an enhanced distressed pavement image. The enhanced image is then decomposed with a three-layer wavelet transform to obtain three texture features of the entire image including High-Amplitude Wavelet Coefficient Percentage (HAWCP), the High-Frequency Energy Percentage (HFEP), and the Standard Deviation (STD). In the meantime, an improved P-tile method is used to obtain the binary image. From the binary image, three additional shape features are extracted including the Average Area of all Connected Components (AA), the Area of the Maximum Connected Component (AM), and the Equivalent Length of the longest Connected Component (EL). Finally, a BP neural network is used to fuse both the texture and shape features sequentially to achieve the initial classification. Experimental results show that for the four types of pavement images, the proposed algorithm achieves an effective classification of the pavement distress image with the accuracy rates of 96.5%, 91.4%, 95.2% and 98.1% respectively, which are higher than those of the classification algorithm with a single-type feature.
4 citations