Flow Direction Level Traffic Flow Prediction Based on a GCN-LSTM Combined Model

In recent years, various types of Intelligent Transportation Systems(ITSs) have produced a large amount of structured traffic flow data. Using these structured data and deep learning models to predict urban short-term traffic flow is an important focus of current research. In order to solve the problem of low accuracy of Long-Short Term Memory(LSTM) model in urban traffic flow prediction, this paper proposes an LSTM model based on attention mechanism. The proposed model can learn the importance of each past value to the current value from the long sequence of traffic data at the past moment, which makes it possible to extract more valuable features. Constructed a dataset using the traffic data in the core section of Wuhan for experiments, and the performance of the improved model is compared with the original LSTM model. The results show that when the input data sequence increases from 16 to 64, the MAPE of our proposed model is reduced by 3.76%, while the MAPE of the LSTM model is reduced by 1.51%, which proves the effectiveness of our proposed method.

Traffic flow prediction remains a significant challenge in intelligent transportation systems. To enhance prediction accuracy, a hybrid model is proposed that integrates an improved Variational Mode Decomposition (VMD), a Residual Spatio-Temporal Graph Convolutional Network (RSTGCN), a Long-term Time Series Informer Network (LTSIN), and an iterative error compensation strategy. This approach effectively addresses the non-stationarity, spatial correlations, and temporal dependencies inherent in traffic data. First, the VMD is optimized using a Crested Porcupine Optimizer algorithm to decompose traffic flow time series into intrinsic mode functions (IMFs), each capturing different traffic patterns. Next, these IMFs are then input into the LTSIN and RSTGCN modules. The LTSIN models long-term temporal dependencies, while the RSTGCN captures short-term temporal and spatial relationships. Residual connectivity within the RSTGCN allows the model to retain node features and aggregate information from neighboring nodes, mitigating the over-smoothing issue typically seen in graph convolutional networks. Lastly, an iterative error correction mechanism is applied, where the IMF-derived predictions are refined and reconstructed to yield the final forecast. The effectiveness of the proposed model is demonstrated by extensive experiments, including ablation experiments, baseline comparisons, and multi-step forecasting experiments. Compared to existing models such as GCN and Informer, the proposed method reduces MAE by 8.78 and 6.79, and MAPE by 6.48% and 5.52%, respectively. These results indicate that the proposed model achieves superior prediction accuracy and can be effectively applied to traffic flow prediction tasks.

Traffic flow prediction accuracy is very important for intelligent transportation systems (ITS). Many studies have proposed different methods for traffic flow prediction including ARIMA, ANN and SVM. With the development of deep learning technology, the evolutionary models of RNN such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units) models have been found to perform well in traffic flow prediction. This paper aims at investigating the use of the Random Forest Regressor Model, an ensemble learning algorithm, for improved and accurate traffic prediction. Random Forest algorithm is highly robust and is well suited for large datasets with many features, thus making it suitable for traffic forecasting. In this research, historical traffic data is used to train the model together with other variables such as traffic flow, weather and time. The Random Forest model performance is compared with the traditional prediction methods using Mean Squared Error. It shows that the Random Forest model is better than the conventional methods and can give better accurate forecasts of traffic flow and can be used in real time traffic management. It presents the actual and predicted vehicle count per hour.

Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. Recent studies have shown that deep learning methods, especially long short-term memory (LSTM) models, have good results in short-term traffic flow prediction. Furthermore, the attention mechanism can properly assign weights to distinguish the importance of traffic time sequences, thereby further improving the computational efficiency of the prediction model. Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. In the data preprocessing module, the missing traffic data are repaired to provide a complete dataset for subsequent prediction. In the prediction module, a combined deep learning method that is an attention-based LSTM (ATT-LSTM) model for predicting short-term traffic speed on urban roads is proposed. The proposed framework was applied to the urban road network in Nanshan District, Shenzhen, Guangdong Province, China, with a 30-day traffic speed dataset (floating car data) used as the experimental sample. Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. The attention mechanism can significantly reduce the error of the LSTM model (up to 12.4%) and improves the prediction performance.

Short-term traffic flow prediction plays an important role in intelligent transportation system. Numerous researchers have paid much attention to it in the past decades. However, the performance of traditional traffic flow prediction methods is not satisfactory, for those methods cannot describe the complicated nonlinearity and uncertainty of the traffic flow precisely. Neural networks were used to deal with the issues, but most of them failed to capture the deep features of traffic flow and be sensitive enough to the time-aware traffic flow data. In this paper, we propose a deep bi-directional long short-term memory (DBL) model by introducing long short-term memory (LSTM) recurrent neural network, residual connections, deeply hierarchical networks and bi-directional traffic flow. The proposed model is able to capture the deep features of traffic flow and take full advantage of time-aware traffic flow data. Additionally, we introduce the DBL model, regression layer and dropout training method into a traffic flow prediction architecture. We evaluate the prediction architecture on the dataset from Caltrans Performance Measurement System (PeMS). The experiment results demonstrate that the proposed model for short-term traffic flow prediction obtains high accuracy and generalizes well compared with other models.

As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control, and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. The traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues, including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we first add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other eight baseline methods on real-world traffic data sets PeMS.

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Traffic flow prediction using spatial-temporal network data remains one of the most important problems in intelligent transportation systems. Timely and accurate traffic prediction is necessary to provide valuable information for different urban planning, traffic control, and guidance tasks. The complexity of the problem is explained by the fact that traffic flows have high nonlinearity and complex spatial-temporal correlations. The development of mathematical models, and, in particular, the deep learning models, allows to use convolutional neural networks to solve traffic prediction problems. In this article, we analyze the architecture of the graph convolution network for traffic flow prediction. The considered graph convolution network takes into account daily and weekly patterns of traffic flow distributions. Experimental studies of the graph neural network model were carried out on the transportation network of Samara city. Experiments show that the considered model outperforms other baseline forecasting algorithms.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

With the continuous development of smart cities, intelligent transportation systems (ITSs) have ushered in many breakthroughs and upgrades. As a solid foundation for an ITS, traffic flow prediction effectively helps the city to better manage intricate traffic flow. However, existing traffic flow prediction methods such as temporal graph convolutional networks(T-GCNs) ignore the dissimilarities between lanes. Thus, they cannot provide more specific information regarding predictions such as dynamic changes in traffic flow direction and deeper lane relationships. With the upgrading of intersection sensors, more and more intersection lanes are equipped with intersection sensors to detect vehicle information all day long. These spatio-temporal data help researchers refine the focus of traffic prediction research down to the lane level. More accurate and detailed data mean that it is more difficult to mine the spatio-temporal correlations between data, and modeling heterogeneous data becomes more challenging. In order to deal with these problems, we propose a heterogeneous graph convolution model based on dynamic graph generation. The model consists of three components. The internal graph convolution network captures the real-time spatial dependency between lanes in terms of generated dynamic graphs. The external heterogeneous data fusion network comprehensively considers other parameters such as lane speed, lane occupancy, and weather conditions. The codec neural network utilizes a temporal attention mechanism to capture the deep temporal dependency. We test the performance of this model based on two real-world datasets, and extensive comparative experiments indicate that the proposed heterogeneous graph convolution model can improve the prediction accuracy.

Multimodal emotion recognition in conversation (MERC) involves predicting the emotion category of a conversation on the basis of textual, acoustic, and visual modalities. Information from these diverse modalities can reinforce each other to enhance the accuracy of emotion prediction. However, some information modalities may be absent in real-world applications and information from various modalities may be difficult to integrate. Therefore, a suitable strategy is required to compensate for missing modalities by using information from the available modalities and prioritizing important information. Consequently, this study developed a graph convolutional network (GCN) model with a feature compensation module and dual-channel second-order pooling module for MERC. This model initially uses a GCN to compensate for missing features by aggregating features corresponding to the same utterance node. Subsequently, it applies dual-channel second-order pooling to sift through and integrate all features. Empirical evaluations of the proposed model against other baseline models on two benchmark datasets, namely the IEMOCAP and MELD datasets, indicated that the proposed model outperformed the other models; this finding underscores the effectiveness of the proposed model in recognizing the emotions expressed in multimodal dialogue data.

ABSTRACTLarge‐area, high‐resolution organic light‐emitting diode (OLED) displays require high‐performance oxide thin‐film transistors (TFTs) with excellent mobility and stability, as well as little threshold voltage shift, especially for G8.5+ OLED production lines. Our previous work demonstrated rare‐earth doped oxides are ideal channel layer materials for TFTs due to their superior mobility and stability. However, scaling up TFT production from pilot lines to mass manufacturing poses significant challenges, largely because of the complexity of multi‐element composite materials and intricate processes. In this study, an end‐to‐end graph convolutional network (GCN) model is employed to optimize TFT production processes. By encoding process parameters as graph nodes and features, the GCN effectively predicts critical drain current–gate voltage (I–V) characteristics, enabling precise calculation of electron mobility, subthreshold slope, and threshold voltage. The model achieves a mean absolute percentage error of 19.9% on validation data. Based on the optimized parameters, a TFT device with properties including a mobility of 37.8 cm2/Vs, a threshold voltage of 0.9 V, and a subthreshold slope of 0.62 V/dec was successfully produced. These results highlight the potential of GCN‐based models to address the complexities of TFT mass production, providing a powerful tool for process optimization and performance enhancement.

Accurate traffic flow forecasting is a crucial need for Intelligent Transportation Systems (ITSs). This supports dynamic and proactive traffic control management. The challenging part lies in reducing the forecast error rate. There was limited success in the previous attempts put forth to develop traffic flow forecasting systems. In this work, the MIDAS Site—UK Highways data is used to predict the traffic flow using the hybrid CNN-LSTM model. The data is preprocessed using Z-Score Normalization in order to scale the data below and above the standard deviation. The features from the preprocessed data are learnt using the CNN model and with these extracted features as input, the LSTM network forecasts the traffic flow. The CNN model, LSTM model and hybrid CNN-LSTM model are trained with the preprocessed data. The trained models are tested for estimating the traffic flow. The results convey that the hybrid CNN-LSTM model proposed forecasts the traffic flow with a reduced error rate.KeywordsTraffic flow forecastingConvolutional Neural Network (CNN)Long Short-Term Memory (LSTM)Hybrid CNN-LSTM modelIntelligent transportation system

Accurate traffic flow prediction is crucial for improving transportation efficiency. To improve the accuracy of traffic flow prediction, we developed a traffic flow prediction framework—namely, traffic flow multicomponent network—that appropriately processes the noise, volatility, and nonlinearity in traffic flow data. This framework comprises three components: a factor selection component, traffic flow decomposition component, and traffic flow prediction component. The factor selection component considers the dynamic effects of weather‐related, environmental, and spatiotemporal factors on traffic flow; it then extracts and analyzes factors exhibiting strong correlations with traffic flow. The traffic flow decomposition component optimizes the parameters of variational mode decomposition on the basis of the envelope entropy by using the sparrow search algorithm; it then transforms traffic flow into multiple intrinsic mode functions to enable accurate traffic flow prediction. Finally, the traffic flow prediction component constructs dynamic feature matrices by using a bidirectional gated recurrent unit model to identify relationships within the data. Moreover, it uses an attention mechanism to assign different weights to different features on the basis of the importance of these features to traffic flow prediction, thereby enabling the efficient processing of a large volume of data. The performance of the proposed framework was examined in experiments conducted on large volumes of traffic flow data with different time granularities. The results indicated that the proposed framework achieved high prediction accuracy and stability for various time granularities, data samples, dataset sizes, and noise conditions. Moreover, it generally outperformed existing traffic flow prediction models under all experimental conditions.

In metropolitan areas, common traffic issues include traffic congestion, traffic accidents, air pollution, and energy consumption occur. To resolve this issues, Intelligent Transportation Systems (ITS) have been evolved by many researchers. One of the important sub-systems in the development of ITS is a Traffic Management System (TMS) which attempts to reduce a traffic congestion. In fact, TMS itself relies on the estimation of traffic flow, therefore providing such an accurate traffic flow prediction is needed. For this reason, we aim to provide an accurate traffic flow prediction to facilitate this system. In this works, a Supervised Deep Learning Based Traffic Flow Prediction (SDLTFP) was proposed which is a type of fully-connected deep neural network (FC-DNN). Timely prediction is also a major issue in guaranteeing reliable traffic flow prediction. However, training a deep network could be time-consuming, and overfitting is might be happening, especially when feeding small data into the deep architecture. The network is learned perfectly during the training, but in testing with the new data, it could fail to generalize the model. We adopt the Batch Normalization (BN) and Dropout techniques to help the network training. SGD and momentum are carried out to update the weight. We then take advantage of open data as historical traffic data which are then used to predict future traffic flow with the proposed method and model above. Experiments show that the Mean Absolute Percentage Error (MAPE) for our traffic flow prediction is within 5 % using sample data and between 15% to 20% using out of the sample data. Training a deep network faster with BN and Dropout reduces the overfitting.