Attention Mechanism-Based Convolutional Model for Collaborative Control of Traffic Signals at Multiple Intersections

The necessity for collaborative traffic signal control optimisation is growing in importance as intelligent transportation technology continues to advance. The study started a related application investigation to successfully raise the degree of cooperative control of traffic lights at several crossings. Based on an in-depth analysis of the traffic scenarios at multiple intersections, the study clarified the key points for optimising the collaborative control of traffic signals. Subsequently, an attention convolution model was introduced to capture the dynamic information changes of key spatial regions and data in the traffic scene through a spatial and temporal attention mechanism. The average vehicle travel time under model control was tested to be 12.4% and 23.3% shorter than the other two methods, respectively. The ablation experiments showed that the model reduced the passing time by about 9.5% and 6.7%. Moreover, the attention is mainly focused on the nodes closer to the location, and the model is more spatially interpretable. The results illustrate that the designed collaborative control of traffic signals at multiple intersections method based on the convolutional model with attention mechanism can enhance the flexibility of traffic management on the basis of improving the efficiency of collaborative control. This offers compelling evidence in favour of building a more clever and effective cooperative traffic signal control system at several crossings.