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With the development of modern cities, urban rail transit has become an indispensable part of residents' travelling mode, and accurate prediction of urban rail transit passenger flow is particularly important. However, due to the non-linearity and non-stability of passenger flow, the low quality of big data and the lack of data make it more and more difficult to predict the passenger flow of urban rail transit. In this paper, we propose a unique model structure that ingeniously integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN) with fused residual network connections, and a self-attention mechanism. The design of this model is primarily to effectively handle complex and low-quality data, which is often prevalent in passenger flow prediction scenarios. To validate the effectiveness of the proposed model, we used data from the Beijing urban rail transit in 2016 for prediction. In the experiments, we performed comparison study and ablation study. The experimental results show that the model proposed in this paper has significantly improved prediction accuracy in both horizontal and vertical comparisons. This outcome substantiates that the proposed model can not only effectively handle complex and low-quality data but also extract short-term features and long-term dependency features of passenger flow well, thereby achieving more accurate predictions. • Integration of GRU, CNN with fused residual connections, and self-attention mechanism constitutes a novel model. • The model is capable of handling complex and lower-quality data, significantly improving the accuracy of predictions. • The model can simultaneously extract short-term features and long-term dependency features from time series. [ABSTRACT FROM AUTHOR]

Predicting the future popularity of commodities has always been a significant issue in information filtering research. Existing methods predominantly rely on the historical popularity of products, assuming that historically popular items will continue to be popular in the future due to preferential attachment. However, this method has limitations as it neglects the intricate structural information within the bipartite networks connecting users and items. The prediction method based on preferential attachment fails for commodities with the same degree of popularity. In this paper, we propose a popularity prediction method that aggregates user recommendation results to forecast item popularity. The method is general and applicable to any recommendation algorithm. For simplicity, we validate the method using the classic collaborative filtering algorithm. Experiments demonstrate that this method significantly outperforms the preferential attachment predictor in accurately predicting the future popularity of niche commodities. [Display omitted] • Preferential attachment fails to work on commodities with the same degree. • The method aggregates recommendations for all users to predict item popularity. • Our method surpasses preferential attachment in niche items popularity prediction. • The method applies to any recommendation algorithm. [ABSTRACT FROM AUTHOR]

Accurate prediction of lane changing (LC) trajectories plays a vital role in ensuring safe and efficient traffic flow on highways. This paper proposes a LC trajectory prediction model based on encoder-decoder architecture to address low long-term prediction accuracy problem and to gain insight into the underlying motivations of LC behavior. Three specific enhancements were proposed to improve the performance of encoder-decoder. The first enhancement involves the utilization of Neighborhood Component Analysis (NCA) to identify the most relevant input features from driving environment. The second enhancement involves the introduction of heuristic attention to capture intricate time-dependent and environment-dependent patterns of input data. Finally, a trajectory check module was proposed to supervise and adjust the prediction outputs based on vehicle dynamics constraints. The proposed model was trained and evaluated using the HighD dataset. Experimental results demonstrated that the proposed model outperforms improved encoder-decoder architectures designed by previous studies in terms of prediction accuracy in long-term prediction, which the lateral and longitude MAE were 0.041 m and 1.842 m in 5 s prediction duration, respectively. Additionally, it was observed that preceding vehicles in the current and target lane, and following vehicle in the target lane, exert a significantly influential effect on LC maneuvers, which represents the underlying motivations behind LC behavior. The findings of this study contribute to the advancement of intelligent transportation systems (ITS) and autonomous vehicles (AVs) by providing an effective LC trajectory prediction. • Neighborhood Component Analysis is employed to identifying and analyzing crucial features and motivations of LC behavior. • An encoder-decoder architecture coupled with time-dependent and environment-dependent attention mechanism can predict the future trajectory accurately in lateral and longitudinal direction. • Trajectory check module can guarantee the rationality of prediction outputs and adjust those unreasonable prediction. [ABSTRACT FROM AUTHOR]

This paper proposes a trajectory prediction method based on graph attention network to accurately predict the trajectories of HDV (Human Drive Vehicles) around the ICV (Intelligent Connected Vehicles) under mixed traffic flow scenario on highways. Firstly, the vehicle trajectory data is filtered and smoothed to construct a trajectory prediction dataset containing map information. Secondly, the vehicle interaction relationship graph is constructed based on the position and behavior of vehicles. The high-dimensional spatial interaction relationship features between the target vehicle and surrounding vehicles are extracted using the graph attention network, which serves as input for the encoder-decoder model. Subsequently, an encoder-decoder model based on GRU (Gate Recurrent Unit) is employed to encode time-series features of vehicle trajectory data and generate future trajectories through decoding. Finally, experimental validation using NGSIM (Next Generation Simulation) datasets demonstrates that our proposed method achieves low displacement error in predicting vehicle trajectories compared to models such as GRU, and CNN-GRU (Convolutional Neural Network-Gate Recurrent Unit). • A trajectory prediction method is proposed to accurately predict the trajectories of HDV (Human Drive Vehicles) around the ICV (Intelligent Connected Vehicles) under mixed traffic flow scenario on highways. • The vehicle trajectory data is filtered and smoothed to construct a trajectory prediction dataset containing map information. • The T-GAT-GRU model is constructed, which can better extract the spatial interaction characteristics between vehicles. [ABSTRACT FROM AUTHOR]

Link prediction is an axial field in complex network analysis as it aims to infer new connections between nodes in a given network. Many applications of this task include: the reference system, the suggestion of friends in the social network and the prediction of interactions between proteins in biological networks. Several methods have been developed in the link prediction task, especially the similarity-based methods which are widely used due to their low complexity and good performance. In this paper, we propose a novel link prediction approach that combines the PageRank algorithm with local information-based methods to improve performance while retaining the advantage of low complexity of local methods. We conducted a series of experimental studies on eleven data sets where we compared our new combined methods with six well-known local methods. The results obtained show a significant gain in terms of performance in almost all data sets. In addition to this and to confirm the superiority of the proposed methods, another comparative study is performed, formed of nine local and global methods. According to the experimental results, our approach outperforms all other compared methods with linear complexity. • The analysis of networks evolution brings us to the problem of link prediction. • Similarity-based methods are used as a local method to predict new links. • New similarity measures between unconnected nodes that do not have shared neighbors. • Our method offers higher precision than some well-known local and global methods. • Our method offers linear computational complexity. [ABSTRACT FROM AUTHOR]