TAMHA-DDPM: A Time-Aware Multi-Head Attention Denoising Diffusion Probability Model for Fuzzy Data Optimization in Intelligent Transportation Systems

In the planning and decision making of transport systems, data relationships are complex and the eigenvalues of data samples may contain errors, resulting in traffic fuzzy data. Traffic fuzzy data has seriously affected the model fitting and lagged the model performance. Optimising the traffic fuzzy data and extracting potentially usable information from it to assist in supporting the decision-making process has become an urgent problem to be solved. In order to solve the data ambiguity problem in the modelling process, this paper improves the structure of denoising diffusion probabilistic model (DDPM), deepens the depth and width of the model, and proposes a Time-Aware Multi-Head Attention Denoising Diffusion Probability Model (TAMHA-DDPM). By integrating the attention mechanism, residual block and affine transformation, replacing the original attention mechanism with a multiple attention mechanism, and using the residual block for up-sampling and down-sampling, the model’s recognition of fuzzy data is greatly improved. Comparison experiments on the parameters of the proposed algorithm on the KITTI dataset show that different degrees of fuzzy data structure reconstruction can be achieved by parameter adjustment, which is also reflected in the evaluation index. When the number of noise-adding rounds is about 200, the data structure is richer, and the performance of related indexes is better; when the number of denoising rounds is about 1000, the data structure is more complete, and the qualitative results are more close to the original data. The time-aware multi-attention denoising diffusion probabilistic model outperforms the existing models in data reconstruction performance, can effectively optimise the fuzzy data in intelligent transportation systems, and the recovered data structure results are more in line with the original data.