## 5 Experiments
### Model Application  
The improved TransD model constructs dynamic mapping matrices for each entity-relation pair, accommodating the diversity of entities and relations while enabling flexible projections that avoid the computational overhead and parameter bloat associated with large static matrices. By embedding nodes and edges into a low-dimensional space that preserves both structural and semantic information, it furnishes a reliable foundation for pattern discovery and relational inference. In the traffic accident domain, a knowledge graph can integrate heterogeneous data--such as accident location, time, weather, and road  
Figure 12: Results of the ablation experiments. conditions--into a semantic network encompassing entities (e.g., vehicles, drivers, environments) and their multifaceted relationships, thereby elucidating deep couplings among risk factors. Although graph neural networks excel at capturing spatiotemporal and topological features in traffic accident data, they often fall short in uniformly modeling multi-source semantic information. This shortcoming motivates the adoption of an enhanced knowledge graph embedding to boost generalization and inference capabilities. Compared to TransE, TransH, TransR, and the baseline TransD, the improved TransD is more robust when handling one-to-many, many-to-one, and many-to-many relations, accurately capturing subtle variations in complex associations such as \"Accident-RoadSegment\" and \"Accident-Weather\". Applying TransD embedding to the self-constructed Traffic Accident Knowledge Graph (TAKG) yields vector representations rich in both semantic and structural cues. These embeddings serve as highly discriminative inputs for downstream tasks--including clustering analysis, anomaly detection, and link prediction--thereby supporting accident root-cause analysis and intelligent safety decision-making. Detailed statistics for TAKG are provided in Table 4.  
As shown in Figure 13, the multidimensional evaluation of the improved TransD model on the self-constructed Traffic Accident Knowledge Graph (TAKG) reveals that, as the embedding dimension increases from 50 to 250, all ranking metrics (MR, MRR, Hits@K) and classification metrics (Accuracy, F1) exhibit clear upward trends, achieving optimal performance at 250 dimensions. Beyond this threshold, slight declines in each metric indicate the onset of overfitting. At 250 dimensions, the model attains its lowest MR and highest MR, with Hits@1, Hits@3, and Hits@10 reaching their peaks, while Accuracy and F1 achieve approximately 0.51 and 0.68, respectively. These results underscore the significant impact of incorporating attribute projection vectors and dual-limit scoring loss within the dynamic mapping mechanism to enhance the representational fidelity and discriminative power of TAKG embeddings. As Table 5 illustrates, in the Mean Rank (MR) metric, the model constrained by low embedding dimensions (50-150) exhibits limited mapping capacity, with entity-ranking errors fluctuating around 985. When the dimension increases beyond 200, the embedding space can better accommodate the additional attribute information introduced by the attribute projection vectors, thereby enlarging the distances between accident and node embeddings. Consequently, MR declines sharply and reaches its minimum--approximately 950--at 250 dimensions. This pattern aligns with the behavior of the dynamic mapping matrices in the TransD framework, which effectively integrates inherent entity attributes and relational semantics. Beyond 250 dimensions, further dimensional increases lead to a steep rise in parameter count and heightened sensitivity to noise, causing a slight rebound in MR--consistent with the overfitting commonly observed in high-dimensional spaces.