Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
Source abstract
Abstract
Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic graph convolutional network model (Res-DGCN) based on the residual network structure for crowd inflow and outflow prediction in urban areas. Firstly, as the attention layer, the spatio-temporal attention module (SA) is employed for capturing the spatial relationship between the target node and the multi-order adjacent nodes by processing the features of the human flow data. Secondly, a conditional convolution module (SCondConv) is used to enhance the model’s capacity for learning about the shifting characteristics of crowd flow to obtain spatial dependence. Finally, we train the model with the Huber loss function to lower the model’s sensitivity to outliers and achieve optimal convergence. In two public datasets, the mean absolute error (MAE) of the proposed model is improved by 5.2% and 9.4%, respectively, compared to the baseline models, and the root mean square error (RMSE) is improved by 4.8% and 8.8%, confirming the model’s usefulness for crowd flow prediction tasks.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Human Mobility and Location-Based Analysis · Traffic and Road Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410081, China
Funding
Natural Science Foundation of Hunan Province · Open Research Project of the State Key Laboratory of Industrial Control Technology · National Defense Science and Technology Key Laboratory Fund Project · National Natural Science Foundation of China · Research Foundation of Education Bureau of Hunan Province, China
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