A Vehicle Path Planning and Prediction Algorithm Based on Attention Mechanism for Complex Traffic Intersection Collaboration in Intelligent Transportation

In the development of smart cities, the transportation system plays a crucial role, with road congestion being particularly prominent under conditions of long-distance travel and high traffic volumes. This paper proposes a Vehicle Path Planning and Prediction Algorithm (VPPPA) based on an attention mechanism for complex traffic intersections collaboration in intelligent transportation systems. Our proposed algorithm is designed to plan and analyze traffic flow at urban road intersections. Firstly, attention mechanisms are used to balance the number of vehicles at different traffic intersections, with a particular focus on alleviating congestion at critical intersections during peak hours. Secondly, a Convolutional Neural Network (CNN) is employed to capture the spatial relationships between different road segments and intersections. Moreover, the Long Short-Term Memory and CNN (LSTM-CNN) architecture effectively captures the important temporal correlations in the traffic flow data. Thirdly, the spatiotemporal attention mechanism of vehicles captures the local spatial correlation characteristics between the target intersection and adjacent intersections along the traffic network. Finally, our proposed VPPPA model leverages the advantages of the LSTM-CNN architecture, enhancing learning efficiency during the training process and extracting valuable information. Experimental results show that the proposed VPPPA has significant advantages and greater efficiency in reducing average travel time and improving throughput across various intersections.