https://doi.org/10.1016/j.ocecoaman.2022.106377

Quantitative analysis of the impact of COVID-19 on ship visiting behaviors to ports- A framework and a case study

Partial Differential Grey Model Based on Control Matrix and Its Application in Short-term Traffic Flow Prediction

Accurate and timely traffic flow prediction not just allows traffic controllers to evade traffic congestion and guarantee standard traffic functioning, it even assists travelers to take advantage of planning ahead of schedule and modifying travel routes promptly. Therefore, short-term traffic flow prediction utilizing artificial intelligence (AI) techniques has received significant attention in smart cities. This manuscript introduces an autonomous short-term traffic flow prediction using optimal hybrid deep belief network (AST2FP-OHDBN) model. The presented AST2FP-OHDBN model majorly focuses on high-precision traffic prediction in the process of making near future prediction of smart city environments. The presented AST2FP-OHDBN model initially normalizes the traffic data using min–max normalization. In addition, the HDBN model is employed for forecasting the traffic flow in the near future, and makes use of DBN with an adaptive learning step approach to enhance the convergence rate. To enhance the predictive accuracy of the DBN model, the pelican optimization algorithm (POA) is exploited as a hyperparameter optimizer, which in turn enhances the overall efficiency of the traffic flow prediction process. For assuring the enhanced predictive outcomes of the AST2FP-OHDBN algorithm, a wide-ranging experimental analysis can be executed. The experimental values reported the promising performance of the AST2FP-OHDBN method over recent state-of-the-art DL models with minimal average mean-square error of 17.19132 and root-mean-square error of 22.6634.

/pdf/autonomous-short-term-traffic-flow-prediction-using-pelican-2dz8ub2i.pdf

Autonomous Short-Term Traffic Flow Prediction Using Pelican Optimization with Hybrid Deep Belief Network in Smart Cities

The traffic sign detection algorithm based on Faster Region-Based Convolutional Neural Network (R-CNN) has been applied to various intelligent-vehicles driving scenarios. However, the model of the current detection algorithm has certain shortcomings, which include the influence of weather and light, the detection of distance traffic signs, and the detection of similar traffic signs. To solve these problems, this paper proposes an improved traffic sign detection method based on Faster R-CNN. First, we propose a fusion method that fuses the feature pyramid into the Faster R-CNN algorithm. This fusion method can extract object features with precision and decrease the influence of weather and light. Second, a deformable convolution (DCN) which can train the algorithm to identify traffic signs with precision and make similar signs more distinguishable, and in particular make it work better with distorted images, is added to the backbone network. Lastly, we apply ROI align to replace the ROI pooling, which can avoid the distant traffic sign detail loss caused by pooling and increase the detection precision of distant traffic signs. The experimental results on both the TT100k dataset and real intelligent vehicle tests demonstrate that the algorithm is superior to the original Faster R-CNN algorithm and four other state-of-the-art methods in traffic sign detection, specifically in small-target traffic sign detection and low-intensity environments such as sunset time and rainy days. Therefore, the proposed method is helpful to improve the traffic sign detection performance in extreme environments (low-light intensity or rainy weather).

/pdf/improved-traffic-sign-detection-algorithm-based-on-faster-r-3edqjj1j.pdf

Improved Traffic Sign Detection Algorithm Based on Faster R-CNN

A graph attention fusion network for event-driven traffic speed prediction

A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism

Understanding the reactivity of carbon surfaces is crucial for the development of advanced functional materials. The SW defect is commonly present in carbon materials, but a comprehensive understanding of its effects on the reactivity of carbons is missing. In this study, we systematically investigate the reactivity of graphene surfaces with the Stone-Wales (SW) defect using Density Functional Theory calculations. We explore the atomic adsorption of various elements, including rows 1–3 of the Periodic Table, potassium, calcium, and selected transition metals. Our results demonstrate that the SW defect enhances binding with the studied adsorbates when compared to pristine graphene, with carbon and silicon showing the most significant differences. Additionally, we examine the effects of mechanical deformation on the lattice by constraining the system with the SW defect to the pristine graphene cell. Interestingly, these constraints lead to even stronger binding interactions. Furthermore, for carbon, nitrogen, and oxygen adsorbates, we observe that mechanical deformation triggers the incorporation of adatoms into the carbon bond network, leading to the reorganization of the SW defect structure. This work establishes a foundation for future studies in the defect and strain engineering of graphene, opening avenues for developing advanced materials and catalysts with enhanced reactivity and performance. 

Reactivity of Stone-Wales defect in graphene lattice – DFT study

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A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism

Reactivity of Stone-Wales defect in graphene lattice – DFT study