An Improved Car-Following Model Accounting for Impact of Strong Wind

In order to investigate the effect of strong wind on dynamic characteristic of traffic flow, an improved car-following model based on the full velocity difference model is developed in this paper. Wind force is introduced as the influence factor of car-following behavior. Among three components of wind force, lift force and side force are taken into account. The linear stability analysis is carried out and the stability condition of the newly developed model is derived. Numerical analysis is made to explore the effect of strong wind on spatial-time evolution of a small perturbation. The results show that the strong wind can significantly affect the stability of traffic flow. Driving safety in strong wind is also studied by comparing the lateral force under different wind speeds with the side friction of vehicles. Finally, the fuel consumption of vehicle in strong wind condition is explored and the results show that the fuel consumption decreased with the increase of wind speed

Phase Plane Analysis Method of Nonlinear Traffic Phenomena

A new phase plane analysis method for analyzing the complex nonlinear traffic phenomena is presented in this paper. This method makes use of variable substitution to transform a traditional traffic flow model into a new model which is suitable for the analysis in phase plane. According to the new model, various traffic phenomena, such as the well-known shock waves, rarefaction waves, and stop-and-go waves, are analyzed in the phase plane. From the phase plane diagrams, we can see the relationship between traffic jams and system instability. So the problem of traffic flow could be converted into that of system stability. The results show that the traffic phenomena described by the new method is consistent with that described by traditional methods. Moreover, the phase plane analysis highlights the unstable traffic phenomena we are chiefly concerned about and describes the variation of density or velocity with time or sections more clearly

Spectrophotometric determinationof trace nitrite with a novel self-coupling diazotizing reagent: J-acid

A simple and sensitive method for the spectrophotometric determination of nitrite was described and optimum reaction conditions along with other important analytical parameters were established. In the presence of potassium bromide at 25°C, nitrite reacted with J-acid in hydrochloric acid producing diazonium salt and then coupled with excess J-acid in the sodium carbonate solution yielding red colored azo compounds. At wavelength of 500 nm, Beer’s law was obeyed over the concentration range of 0,02 – 0,60 mg∙L⁻¹. The molar absorptivity was 3,92∙10⁴ L∙mol⁻¹∙cm⁻¹. This method was easily applied to the determination of trace nitrite in environmental water with recoveries of 9₈,7 – 101,2%

Carotid Atherosclerosis Detected by Ultrasonography: A National Cross‐Sectional Study

Background: Carotid atherosclerosis (CA) is a reflector of generalized atherosclerosis that is associated with systemic vascular disease. Data are limited on the epidemiology of carotid lesions in a large, nationally representative population sample. We aimed to evaluate the prevalence of CA detected by carotid ultrasonography and related risk factors based on a national survey in China. Methods and Results: A total of 107 095 residents aged ≥40 years from the China National Stroke Prevention Project underwent carotid ultrasound examination. Participants with carotid endarterectomy or carotid stenting and those with stroke or coronary heart disease were excluded. Data from 84 880 participants were included in the analysis. CA was defined as increased intima–media thickness (IMT) ≥1 mm or presence of plaques. Of the 84 880 participants, 46.4% were men, and the mean age was 60.7±10.3 years. The standardized prevalence of CA was 36.2% overall, increased with age, and was higher in men than in women. Prevalence of CA was higher among participants living in rural areas than in urban areas. Approximately 26.5% of participants had increased IMT, and 13.9% presented plaques. There was an age‐related increase in participants with increased IMT, plaque presence, and stenosis. In multiple logistic regression analysis, older age, male sex, residence in rural areas, smoking, alcohol consumption, physical inactivity, obesity, hypertension, diabetes mellitus, and dyslipidemia were associated with CA. Conclusions: CA was highly prevalent in a middle‐aged and older Chinese population. This result shows the potential clinical importance of focusing on primary prevention of atherosclerosis progression

COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference

The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments. This framework includes an Iterated Trajectory Mean Shift (ITMS) module to localize road centerlines, which copes with noisy GPS data points. Convolutional Neural Network trained on our novel trajectory descriptor is then introduced into our framework to detect and accurately classify junctions for refinement of the road maps. COLTRANE yields up to 37% improvement in F1 scores over existing methods on two distinct real-world datasets: city roads and airport tarmac.Comment: BuildSys 201

Asymmetric cost aggregation network for efficient stereo matching

Abstract Cost aggregation is crucial to the accuracy of stereo matching. A reasonable cost aggregation algorithm should aggregate costs within homogeneous regions where pixels have the same or similar disparities. Otherwise, the estimated disparity map is prone to the well‐known edge‐fattening issue and the problem of losing fine structures. However, current state‐of‐the‐art convolutional neural networks (CNNs) mainly do cost aggregation with square‐kernel convolutional layers that learn to adjust their kernel elements to make the actual receptive fields of the aggregated costs adapt to homogeneous regions with various shapes. This is a mechanism that easily leads to the above issues due to the translation equivalence and content‐insensitivity properties of CNNs. To tackle these problems, a novel densely connected asymmetric convolution block (Dense‐ACB) based on asymmetric convolutions is proposed to explicitly construct receptive fields with various shapes, which effectively alleviates the issues caused by mismatching shapes of receptive fields and homogeneous regions. The proposed Dense‐ACB brings new insight to CNN‐based stereo matching networks. Based on the proposed cost aggregation method, an efficient and effective stereo matching network is built, which not only achieves competitive overall accuracy compared with state‐of‐the‐art models but also effectively alleviates the edge‐fattening problem and preserves fine structures