In this study, the authors propose a novel component-wise variable step-size (CVSS) diffusion distributed algorithm for estimating a specific parameter over sensor networks. The novelty of the CVSS algorithm is that stepsizes vary from each other on different components at each iteration. They derive the steady-state value of global mean-square deviation (MSD) and relative MSD (RMSD). In the numerical simulations, they compare the proposed CVSS algorithm with several other least mean square (LMS) algorithms. Results show that, when compared with these other algorithms, the CVSS algorithm can effectively reduce steady-state value and speed up convergence rate of RMSD while not sacrificing the convergence rate of MSD. Results also reveal that the proposed CVSS algorithm can achieve reduced difference of steady-state values of relative estimation error on various components.
Short-term traffic speed prediction has become one of the most important parts of intelligent transportation systems (ITSs). In recent years, deep learning methods have demonstrated their superiority both in accuracy and efficiency. However, most of them only consider the temporal information, overlooking the spatial or some environmental factors, especially the different correlations between the target road and the surrounding roads. This paper proposes a traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA) to address the above issues. We apply temporal clustering to the target road to distinguish the traffic environment. Traffic data in each cluster have a similar distribution, which can help improve the prediction accuracy. A hierarchical attention-based mechanism is then used to extract the features at each time step. The encoder measures the importance of spatial features, and the decoder measures the temporal ones. The proposed method is evaluated over the data of a certain area in Hangzhou, and experiments have shown that this method can outperform the state of the art for traffic speed prediction.
Traffic network partitioning is of great importance in regional coordinated traffic signal control in urban areas. Most partitioning algorithms only use a single traffic parameter to represent dynamic traffic information, which will lead to inaccurate results. Moreover, traditional clustering and heuristic partitioning algorithms are not practical in applications. Thus, in this paper, we first propose a new combinatorial characteristic parameter for clustering-based partitioning algorithm by using the Pearson correlation coefficient and data normalization. Then, we refer to the idea of ''snake'' algorithm and use a linear programming model to obtain the exact partitioning result, and such algorithm avoids local optimum of heuristic algorithms. Finally, based on the real traffic data of a Chinese city, we conduct the experiments and verify the effectiveness of the new combinatorial parameter.INDEX TERMS Intelligent transportation systems, partitioning algorithms, clustering methods, correlation, linear programming.
The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and date attributes on AS prediction has been analyzed. Then, through spatiotemporal correlations calculation based on the data from Global Positioning System (GPS), the predictive method utilizes the recursive least squares method to fuse the historical AS with other factors (such as weather, date attributes, etc.) and adopts an extended Kalman filter algorithm to accurately predict the AS of the target segment. Finally, we applied our approach on the traffic congestion prediction on four road segments in Chengdu, China. The results showed that the proposed predictive model is highly feasible and accurate.
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