| Summary: | Traffic flow prediction is a crucial aspect of Intelligent Transport Systems, offering a scientific foundation for urban transport system management and planning. However, predicting traffic flow becomes challenging due to its susceptibility to diverse static and dynamic external factors, such as the presence of emergency vehicles that necessitate priority treatment in the road network. To tackle this issue, this paper introduces an Emergency Vehicle Priority Scheduling Model based on Heterogeneous Feature Fusion in Graph Convolutional Networks (EVHF-GCN). This model concurrently considers road network and emergency vehicle information, dynamically adjusting signal control strategies based on traffic flow prediction outcomes. This approach ensures the prioritized passage of emergency vehicles and mitigates traffic congestion. The model utilizes a heterogeneous feature fusion mechanism within a Graph Convolutional Network (GCN) to propagate features and aggregate information from intersection nodes. It also integrates a Gated Recurrent Unit (GRU) network to capture dynamic traffic flow features. Additionally, we propose a Dynamic Signal Control Strategy (DSCS) that determines intersection green light durations based on prediction results and selects different control strategies as per the situation. Experimental results demonstrate that the model enhances traffic flow prediction accuracy and improves traffic system efficiency and safety in scenarios with and without emergency vehicles.
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