To satisfy the growing demand for traffic prediction induced by urbanization, the intelligent transportation system integrated various cutting-edge artificial intelligence technologies, with large language models (LLMs) as a representative, has been developed. However, existing methods are mostly confined by shallow LLMs utilization, where the semantic capacity of LLMs is ignored and the traffic data are directly fed in. Furthermore, the modality diversity of different traffic prediction scenarios (e.g., flow, speed, and demanding) remains to be underexplored, which restricts the model flexibility towards downstream applications. To mitigate these limitations, we propose a Mixture of Semantic and Spatial Experts (SS-MoE) for traffic prediction along with the human-intelligible post-hoc result explanation. Specifically, to enlighten the traffic predictor with abundant semantic information, we design hierarchically coarse- and fine-grained prompts including role assignments, dataset descriptions, and background supplements, which serves as the auxiliary knowledge for downstream prediction. Afterwards, considering the diversity of real-world traffic scenarios, we construct the MoE framework consisting of a spatial expert, a semantic expert, and a general expert, which accounts for the node-level features, the semantic representations, and the overall generalization, respectively. At last, we instruct the LLM to explain and analyze the final prediction, which is able to provide insightful conclusions and support intelligent transportation decisions, forming a unified prediction-explanation pipeline. Extensive experiments on five public traffic datasets demonstrate the superiority of SS-MoE across three traffic prediction tasks. Experimental results indicate that the MAE and RMSE values of SS-MoE are reduced by up to 4.04% and 3.20% compared with that of the runner-up, respectively.

Accurate traffic flow prediction is a key aspect of building data-driven intelligent transportation systems (ITSs) which relies on the Internet of Things (IoT) sensors deployed along roads, and dynamic spatial-temporal dependencies mining is a major area of interest in traffic flow prediction. Existing methods, however, overlook the diversities of traffic flow patterns from the perspectives of temporal and spatial dimensions. To this end, this article presents a multiview spatial-temporal adaptive transformer-GRU (MST-ATG) framework based on the encoder-decoder architecture to capture complex spatial-temporal dependencies from various perspectives. Specifically, a multiview embedding layer (MEL) containing original traffic data and spatial-temporal correlated features is designed to enrich the feature encoding. Then, based on the inherent characteristics of traffic flow, we introduce a periodicity-trend decomposition (PTD) method to fully consider the periodic- and trend-oriented features of time series. Finally, we propose a spatial-temporal adaptive transformer-GRU (ST-ATG) to dynamically extract spatial-temporal dependencies and adaptively choose computation steps in which a temporal adaptive stacked-GRU module (T-AGM) is proposed to extract correlations in temporal dimension and spatial dependencies captured by a spatial adaptive transformer module (S-ATM). Experimental results on six large-scale real-world datasets demonstrate that our MST-ATG framework outperforms the benchmarks in prediction accuracy. For instance, the average root-mean-square error of MST-ATG on PeMS08 is reduced by 48.3%, 41.09%, 12.95%, 17.67%, 18.64%, 2.4%, 14.67%, 9.15%, 1.1%, 2.4%, 2.51%, and 1.2% compared to that of autoregressive integrated moving average, long short-term memory (LSTM), DCRNN, STGCN, ASTGCN, GWNet, STSGCN, AGCRN, Bi-STAT, STAEformer, PDFormer, and STPGNN, respectively.

To address the imbalance problem between supply and demand for taxis and passengers, this paper proposes a distributed ensemble empirical mode decomposition with normalization of spatial attention mechanism based bi-directional gated recurrent unit (EEMDN-SABiGRU) model on Spark for accurate passenger hotspot prediction. It focuses on reducing blind cruising costs, improving carrying efficiency, and maximizing incomes. Specifically, the EEMDN method is put forward to process the passenger hotspot data in the grid to solve the problems of non-smooth sequences and the degradation of prediction accuracy caused by excessive numerical differences, while dealing with the eigenmodal EMD. Next, a spatial attention mechanism is constructed to capture the characteristics of passenger hotspots in each grid, taking passenger boarding and alighting hotspots as weights and emphasizing the spatial regularity of passengers in the grid. Furthermore, the bi-directional GRU algorithm is merged to deal with the problem that GRU can obtain only the forward information but ignores the backward information, to improve the accuracy of feature extraction. Finally, the accurate prediction of passenger hotspots is achieved based on the EEMDN-SABiGRU model using real-world taxi GPS trajectory data in the Spark parallel computing framework. The experimental results demonstrate that based on the four datasets in the 00-grid, compared with LSTM, EMD-LSTM, EEMD-LSTM, GRU, EMD-GRU, EEMD-GRU, EMDN-GRU, CNN, and BP, the mean absolute percentage error, mean absolute error, root mean square error, and maximum error values of EEMDN-SABiGRU decrease by at least 43.18%, 44.91%, 55.04%, and 39.33%, respectively.

With the rapid development of data-driven intelligent transportation systems, an efficient route recommendation method for taxis has become a hot topic in smart cities. We present an effective taxi route recommendation approach (called APFD) based on the artificial potential field (APF) method and Dijkstra method with mobile trajectory big data. Specifically, to improve the efficiency of route recommendation, we propose a region extraction method that searches for a region including the optimal route through the origin and destination coordinates. Then, based on the APF method, we put forward an effective approach for removing redundant nodes. Finally, we employ the Dijkstra method to determine the optimal route recommendation. In particular, the APFD approach is applied to a simulation map and the real-world road network on the Fourth Ring Road in Beijing. On the map, we randomly select 20 pairs of origin and destination coordinates and use APFD with the ant colony (AC) algorithm, greedy algorithm (A*), APF, rapid-exploration random tree (RRT), non-dominated sorting genetic algorithm-II (NSGA-II), particle swarm optimization (PSO), and Dijkstra for the shortest route recommendation. Compared with AC, A*, APF, RRT, NSGA-II, and PSO, concerning shortest route planning, APFD improves route planning capability by 1.45%–39.56%, 4.64%–54.75%, 8.59%–37.25%, 5.06%–45.34%, 0.94%–20.40%, and 2.43%–38.31%, respectively. Compared with Dijkstra, the performance of APFD is improved by 1.03–27.75 times in terms of the execution efficiency. In addition, in the real-world road network, on the Fourth Ring Road in Beijing, the ability of APFD to recommend the shortest route is better than those of AC, A*, APF, RRT, NSGA-II, and PSO, and the execution efficiency of APFD is higher than that of the Dijkstra method.

Alternative splicing enables a gene translating into different isoforms and into the corresponding proteoforms, which actually accomplish various biological functions of a living body. Isoform-isoform interactions (IIIs) provide a higher resolution interactome to explore the cellular processes and disease mechanisms than the canonically studied protein-protein interactions (PPIs), which are often recorded at the coarse gene level. The knowledge of IIIs is critical to map pathways, understand protein complexity and functional diversity, but the known IIIs are very scanty. In this paper, we propose a deep learning based method called DeepIII to systematically predict genome-wide IIIs by integrating diverse data sources, including RNA-seq datasets of different human tissues, exon array data, domain-domain interactions (DDIs) of proteins, nucleotide sequences and amino acid sequences. Particularly, DeepIII fuses these data to learn the representation of isoform pairs with a four-layer deep neural networks, and then performs binary classification on the learnt representation to achieve the prediction of IIIs. Experimental results show that DeepIII achieves a superior prediction performance to the state-of-the-art solutions and the III network constructed by DeepIII gives more accurate isoform function prediction. Case studies further confirm that DeepIII can differentiate the individual interaction partners of different isoforms spliced from the same gene. The code and datasets of DeepIII are available at http://mlda.swu.edu.cn/codes.php?name=DeepIII.

Distributed optimization has been well developed in recent years due to its wide applications in machine learning and signal processing. In this paper, we focus on investigating distributed optimization to minimize a global objective. The objective is a sum of smooth and strongly convex local cost functions which are distributed over an undirected network of n nodes. In contrast to existing works, we apply a distributed heavy-ball term to improve the convergence performance of the proposed algorithm. To accelerate the convergence of existing distributed stochastic first-order gradient methods, a momentum term is combined with a gradient-tracking technique. It is shown that the proposed algorithm has better acceleration ability than GT-SAGA without increasing the complexity. Extensive experiments on real-world datasets verify the effectiveness and correctness of the proposed algorithm.

This article considers a category of constrained convex optimization problems over multiagent networks. The networked agents aim at collaboratively minimizing the sum of all locally known objective functions over a common convex set. Each agent possesses only its local convex function and its state is constrained to a privately known convex set. A novel distributed algorithm is proposed over time-varying unbalanced directed networks based on epigraph form of the original optimization problem and consensus theory. By incorporating the random sleep scheme, the proposed algorithm allows each agent to independently and randomly decide whether to calculate subgradient and take projection at each iteration, which alleviates the cost of subgradient observation. Besides, it neither resorts to doubly stochastic weight matrices (but only row-stochastic) nor the information of the graph sequence to execute. The convergence of the algorithm is explicitly analyzed under conditions that the sequence of time-varying directed graphs is uniformly jointly strongly connected and the subgradients of all local objective functions are bounded over a convex set. The optimization algorithm ensures zero-gap on the expected distance between the estimated value of each agent and the exact optimal solution. The two simulation cases are presented to demonstrate the practicability of the algorithm and correctness of the obtained theoretical results.

Network traffic classification plays a significant role in cyber security applications and management scenarios. Conventional statistical classification techniques rely on the assumption that clean labelled samples are available for building classification models. However, in the big data era, mislabelled training data commonly exist due to the introduction of new applications and lack of knowledge. Existing statistical traffic classification techniques do not address the problem of mislabelled training data, so their performance become poor in the presence of mislabelled training data. To meet this challenge, in this paper, we propose a new scheme, Noise-resistant Statistical Traffic Classification (NSTC), which incorporates the techniques of noise elimination and reliability estimation into traffic classification. NSTC estimates the reliability of the remaining training data before it builds a robust traffic classifier. Through a number of traffic classification experiments on two real-world traffic data sets, the results show that the new NSTC scheme can effectively address the problem of mislabelled training data. Compared with the state of the art methods, NSTC can significantly improve the classification performance in the context of big unclean data.

Traffic classification plays the significant role in the network security and management. However, accurate classification is challenging if the training data is contaminated with unclean traffic. Recent researches often assume clean training data, and hence performance reduced on real-time network traffic. To meet this challenge, in this paper, we propose a robust method, Unclean Traffic Classification (UTC), which incorporates noise elimination and suspected noise reweighting. Firstly, UTC eliminates strong noisy training data identified by a consensus filtering with multiple classifiers. Furthermore, UTC estimates the relevance of remaining training data and learns a robust traffic classifier. Through a number of experiments on a real-world traffic dataset, we show that the new method outperforms existing state-of-the-art traffic classification methods, under the extremely difficult circumstance with unclean training data.