Accurate short-term traffic forecasts help people choose transportation and travel time. Through the query data, many models for traffic flow prediction have neglected the temporal and spatial correlation of traffic flow, so that the prediction accuracy is limited by the accuracy of traffic data. This paper proposed a short-term traffic flow prediction model that combined the spatio-temporal analysis with a Gated Recurrent Unit (GRU). In the proposed prediction model, firstly, time correlation analysis and spatial correlation analysis were performed on the collected traffic flow data, and then the spatiotemporal feature selection algorithm was employed to define the optimal input time interval and spatial data volume. At the same time, the selected traffic flow data were extracted from the actual traffic flow data and converted into a two-dimensional matrix with spatio-temporal traffic flow information. The GRU was used to process the spatio-temporal feature information of the internal traffic flow of the matrix to achieve the purpose of prediction. Finally, the prediction results obtained by the proposed model were compared with the actual traffic flow data to verify the effectiveness of the model. The model proposed in this paper was compared with the convolutional neural network (CNN) model and the GRU model, and the results show that the proposed method outperforms both in accuracy and stability.

/pdf/short-term-traffic-flow-prediction-method-for-urban-road-1r6eu7yt0s.pdf

Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU

The development of Internet of Vehicles (IoV) has produced a considerable amount of real-time traffic data. These traffic data constitute a kind of digital twin that connects the physical vehicles and their virtual representation via 5G communications. Generally, through analyzing the digital twin traffic data, traffic administrators can optimize traffic scheduling and alleviate traffic jams. However, the exceptions of IoV sensors inevitably raise an issue of traffic data sparsity and consequently influence scientific traffic scheduling decisions. Inspired by this drawback, in this article, a digital twin-assisted real-time traffic data prediction method is proposed by analyzing the traffic flow and velocity data monitored by IoV sensors and transmitted through 5G. At last, we conduct a set of experiments based on a traffic dataset collected by Nanjing city of China. Reported results show the feasibility of our proposal in smart traffic flow and velocity prediction that call for a quick response and high accuracy. 

Digital Twin-Assisted Real-Time Traffic Data Prediction Method for 5G-Enabled Internet of Vehicles

Short-term electrical load forecasting is of great significance to the safe operation, efficient management, and reasonable scheduling of the power grid. However, the electrical load can be affected by different kinds of external disturbances, thus, there exist high levels of uncertainties in the electrical load time series data. As a result, it is a challenging task to obtain accurate forecasting of the short-term electrical load. In order to further improve the forecasting accuracy, this study combines the data-driven long-short-term memory network (LSTM) and extreme learning machine (ELM) to present a hybrid model-based forecasting method for the prediction of short-term electrical loads. In this hybrid model, the LSTM is adopted to extract the deep features of the electrical load while the ELM is used to model the shallow patterns. In order to generate the final forecasting result, the predicted results of the LSTM and ELM are ensembled by the linear regression method. Finally, the proposed method is applied to two real-world electrical load forecasting problems, and detailed experiments are conducted. In order to verify the superiority and advantages of the proposed hybrid model, it is compared with the LSTM model, the ELM model, and the support vector regression (SVR). Experimental and comparison results demonstrate that the proposed hybrid model can give satisfactory performance and can achieve much better performance than the comparative methods in this short-term electrical load forecasting application.

Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting

The problem of short-term travel time estimation has been intensively investigated recently However, accurate travel time predicting is still a challenge due to dynamic changes of the traffic and the difficulty of extracting urban traffic data features In this paper, we mainly focus on time shifting feature of urban roads, which represents the impact of the upstream sections that will be conveyed to the downstream sections after a certain period of time \(\varDelta {t}\) Firstly, we obtain the spatial relationships of the traffic time with Kullback-Leibler divergence (KL-divergence) and urban road networks Then a Convolutional Neural Network (CNN) module is adopted to extract the spatial-temporal and time shifting information of the target road Finally, a novel deep architecture combined CNN and Long-short Term Memory Recurrent Neural Network (LSTM) is utilized to predict the short-term travel time The experimental result on the real data set shows that the proposed model is more effective than other existing approaches

Travel Time Forecasting with Combination of Spatial-Temporal and Time Shifting Correlation in CNN-LSTM Neural Network

A deep network with analogous self-attention for short-term traffic flow prediction

In this paper, a data grouping approach based on convolutional neural network (DGCNN) is proposed for forecasting urban short-term traffic flow. This approach includes the consideration of spatial relations between traffic locations, and utilizes such information to train a convolutional neural network for forecasting. There are three advantages of our approach: (1) the spatial relations of traffic flow are adopted; (2) high-quality features are extracted by CNN; and (3) the accuracy of forecasting short-term traffic flow is improved. To verify our model, extensive experiments are performed on a real data set, and the result shows that the model is more effective than other existing methods.

A Data Grouping CNN Algorithm for Short-Term Traffic Flow Forecasting

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A Data Grouping CNN Algorithm for Short-Term Traffic Flow Forecasting