In the evolution of Intelligent Transportation Systems (ITS), traffic flow prediction has played a pivotal role [1]. Accurate and real-time traffic forecasting is not only a fundamental component of ITS but also a key enabler for efficient urban operation and intelligent mobility development [2, 3]. With the rapid increase in private vehicle ownership, particularly in fast-growing economies, urban road networks have become increasingly congested, and major intersections and arterial roads often experience persistent traffic jams [4]. By accurately predicting traffic flow over short time intervals at critical intersections, transportation authorities can make informed decisions on traffic control and road planning, reduce accidents and delays, and provide travelers with reasonable route recommendations, thereby alleviating traffic pressure and maximizing the utilization of road resources. Figure 1 shows the traffic flow distribution scene at a typical four-way intersection on a city road. In traditional traffic flow prediction studies, various modeling approaches have been proposed, ranging from classical time series models (such as ARIMA) to machine learning and deep learning frameworks (such as RNN and LSTM) [5]. Although these single-model approaches can achieve satisfactory planning performance under controlled conditions [6], their generalization and robustness are often limited by the highly dynamic and nonlinear nature of urban traffic systems [7]. Moreover, most existing models primarily emphasize prediction accuracy while overlooking critical aspects such as computational efficiency, adaptability, and scalability, which are essential for real-time applications in large-scale traffic networks [8]. To address the aforementioned limitations, hybrid and decomposition-based modeling approaches have attracted growing research interest.

As a core technology of Intelligent Transportation System (ITS), traffic flow prediction has a wide range of applications. Traffic flow data are spatial-temporal, which are not only correlated to spatial locations in road networks, but also vary with temporal time indices. Existing methods have solved the challenges in traffic flow prediction partly, focusing on modeling spatial-temporal dependencies effectively, while not all intrinsic properties of traffic flow data are utilized fully. Besides, there are very few attempts at incremental learning of spatial-temporal data mining, and few previous works can be easily transferred to the traffic flow prediction task. Motivated by the challenge of incremental learning methods for traffic flow prediction and the underutilization of intrinsic properties of road networks, we propose a Transport-Hub-aware Spatial-Temporal adaptive graph transFormer (H-STFormer) for traffic flow prediction. Specifically, we first design a novel spatial self-attention module to capture the dynamic spatial dependencies. Three graph masking matrices are integrated into spatial self-attentions to highlight both short- and long-term dependences. Additionally, we employ a temporal self-attention module to detect dynamic temporal patterns in the traffic flow data. Finally, we design an extra spatial-temporal knowledge distillation module for incremental learning of traffic flow prediction tasks. Through extensive experiments, we show the effectiveness of H-STFormer in normal and incremental traffic flow prediction tasks. The code is available at https://github.com/Fantasy-Shaw/H-STFormer.

Traffic flow forecasting is challenging due to the intricate spatio-temporal correlations in traffic flow data. Existing Transformer-based methods usually treat traffic flow forecasting as multivariate time series (MTS) forecasting. However, too many sensors can cause a vector with a dimension greater than 800, which is difficult to process without information loss. In addition, these methods design complex mechanisms to capture spatial dependencies in MTS, resulting in slow forecasting speed. To solve the abovementioned problems, we propose a Fast Pure Transformer Network (FPTN) in this paper. First, the traffic flow data are divided into sequences along the sensor dimension instead of the time dimension. Then, to adequately represent complex spatio-temporal correlations, Three types of embeddings are proposed for projecting these vectors into a suitable vector space. After that, to capture the complex spatio-temporal correlations simultaneously in these vectors, we utilize Transformer encoder and stack it with several layers. Extensive experiments are conducted with 4 real-world datasets and 13 baselines, which demonstrate that FPTN outperforms the state-of-the-art on two metrics. Meanwhile, the computational time of FPTN spent is less than a quarter of other state-of-the-art Transformer-based models spent, and the requirements for computing resources are significantly reduced.

To address the above challenges, we propose STC-GEF, the novel Spatio-Temporal Cross-platform Graph Embedding Fusion approach In this paper, we have proposed STC-GEF, a novel Spatio-Temporal for the urban taxi flow prediction. In this prototype study, Cross-platform Graph Embedding Fusion approach for the urban we have made the following three major contributions: traffic flow prediction. We have designed a spatial embedding module based on graph convolutional networks (GCN) to extract the (1) Spatial and Temporal Graph Embedding Learning: To complex spatial features within the traffic flow data. Furthermore, extract the complex spatial features within traffic flow data, to capture the temporal dependencies between the traffic flow data we propose a spatial embedding module based on graph from various time intervals, we have designed a temporal embedding convolutional networks (GCN). Additionally, to capture the module based on recurrent neural networks. Based on the temporal dependencies between the traffic flow data from observations that different transportation platforms' trip data (e.g., various time intervals, we leverage a temporal embedding taxis, Uber, and Lyft) can be correlated, we have designed an effective module based on recurrent neural networks.

Accurate real-time traffic forecast is critical for intelligent transportation systems (ITS) and it serves as the cornerstone of various smart mobility applications. Though this research area is dominated by deep learning, recent studies indicate that the accuracy improvement by developing new model structures is becoming marginal. Instead, we envision that the improvement can be achieved by transferring the "forecasting-related knowledge" across cities with different data distributions and network topologies. To this end, this paper aims to propose a novel transferable traffic forecasting framework: Domain Adversarial Spatial-Temporal Network (DASTNet). DASTNet is pre-trained on multiple source networks and fine-tuned with the target network's traffic data. Specifically, we leverage the graph representation learning and adversarial domain adaptation techniques to learn the domain-invariant node embeddings, which are further incorporated to model the temporal traffic data. To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems. DASTNet consistently outperforms all state-of-the-art baseline methods on three benchmark datasets. The trained DASTNet is applied to Hong Kong's new traffic detectors, and accurate traffic predictions can be delivered immediately (within one day) when the detector is available. Overall, this study suggests an alternative to enhance the traffic forecasting methods and provides practical implications for cities lacking historical traffic data.

Artificial intelligence and HPC system have the potential to transform the automotive industries by researching and redesigning the process. FREMONT, CA: Artificial intelligence (AI) holds the key to a new future of value for the automotive industry. According to a report, the amount of Artificial Intelligence in automotive manufacturing and cloud services will exceed $10.73 billion by 2024. The advanced capabilities of AI, coupled with increasing consumer expectations, have pushed the automotive industry into a period of digital transformation. These manufacturing techniques increase the use of computer vision for defect detection.

Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model. It compares the traffic data time singularity with the probability value in the dropout module and combines them at unequal time intervals to achieve an accurate prediction of traffic flow data. Then, we design an adaptive traffic flow embedded system that can adapt to Java, Python and other languages and other interfaces. The experimental results demonstrate that the method based on the SDLSTM - ARIMA model has higher accuracy than the similar method using only autoregressive integrated moving average or autoregressive. Our embedded traffic prediction system integrating computer vision, machine learning and cloud has the advantages such as high accuracy, high reliability and low cost. Therefore, it has a wide application prospect.

Spatial time series forecasting problems arise in a broad range of applications, such as environmental and transportation problems. These problems are challenging because of the existence of specific spatial, short-term and long-term patterns, and the curse of dimensionality. In this paper, we propose a deep neural network framework for large-scale spatial time series forecasting problems. We explicitly designed the neural network architecture for capturing various types of patterns. In preprocessing, a time series decomposition method is applied to separately feed short-term, long-term and spatial patterns into different components of a neural network. A fuzzy clustering method finds cluster of neighboring time series based on similarity of time series residuals; as they can be meaningful short-term patterns for spatial time series. In neural network architecture, each kernel of a multi-kernel convolution layer is applied to a cluster of time series to extract short-term features in neighboring areas. The output of convolution layer is concatenated by trends and followed by convolution-LSTM layer to capture long-term patterns in larger regional areas. To make a robust prediction when faced with missing data, an unsupervised pretrained denoising autoencoder reconstructs the output of the model in a fine-tuning step. The experimental results illustrate the model outperforms baseline and state of the art models in a traffic flow prediction dataset.

Real-time traffic flow prediction can not only provide travelers with reliable traffic information and thus save time, but also assist traffic management department to manage transportation system. It can greatly improve the efficiency of transportation. Traditional traffic flow prediction methods usually need a huge amount of data but still leaves a poor performance. With the development of deep learning, researchers begin to pay attention to artificial neural networks (ANNs) such as RNN and LSTM. However, these ANNs are very time-consuming. In our article, we improve the Deep Residual Network and build a dynamic model which previous researchers hardly use. Our result shows that our model can not only be trained efficiently but also have a higher accuracy. Additionally, our dynamic model is more suitable for practical applications.