ST-DDGAN: A Traffic Data Compensation Model Based on Image Restoration Technology

In Intelligent Transportation Systems (ITS), the accuracy of compensating for missing traffic data is critical. This directly impacts the effectiveness of traffic flow prediction and road condition monitoring. Inspired by image restoration techniques, this study introduces the Generative Adversarial Network (GAN) to enhance traffic data compensation. Firstly, to address the problem of converting traffic data into the traffic flow matrix of the road network, we propose the RoadNetIMatrix algorithm to generate the traffic flow matrix of the road network. This algorithm precisely captures traffic flow dynamics in road networks and provides a holistic representation of traffic states. Secondly, given the inherent spatio-temporal correlation in traffic data, we proposed a spatio-temporal collaborative mining component (STSSM). This component integrates the hidden temporal dependencies and spatial features of the mined traffic data into the GAN generator to improve the authenticity of the generated content and ensure the consistency of data compensation. Finally, addressing the influence of external characteristics of traffic data on data compensation results, an external information module based on a multi-head attention mechanism is constructed, which can effectively mine the influence of external factors of traffic data. Furthermore, spatio-temporal and external features are fused to further improve the accuracy of data compensation. Experiments show that the model has a higher accuracy of data compensation and a better generalization of the system in the case of multiple types or a high data loss rate.