## 7 Conclusions  
The California Traffic Accident Knowledge Graph (TAKG) developed in this study, together with the Enhanced TransD-based representation method, provides multidimensional, interpretable tools for accident-risk prediction and causal analysis to traffic management authorities, emergency response agencies, and urban planners. Traffic agencies may leverage the knowledge graph to identify high-risk road segments and characteristic accident factors, thereby guiding targeted remediation and prevention measures. Emergency responders can use model outputs to optimize deployment and improve response efficiency. Urban planners can assess the safety of proposed roads or intersections and validate design alternatives via simulation platforms, facilitating intelligent traffic safety management and data-driven decision optimization. This study addresses the challenges posed by the high-dimensional heterogeneity and complex mapping relationships inherent in traffic accident data by systematically developing a knowledge graph construction framework and an enhanced representation-learning method. The main conclusions are as follows:  
1. Knowledge Graph Construction: Guided by a top-down ontology design principle, we constructed a multi-layered traffic accident knowledge graph that integrates entities such as accident events, involved parties, victims, and environmental factors. This graph efficiently consolidates over 120 accident attributes and supports intuitive visualization, thereby providing a unified data platform for causal relationship mining and risk prediction. 2. Improved TransD Model: By introducing attribute projection vectors, domain-specific features--such as \"accident severity\" and \"road slipperiness\"--are explicitly encoded into the dynamic mapping matrices. Furthermore, a dual-limit scoring loss enforces clearer separation between positive and negative samples in the embedding space. This enhancement balances representational expressiveness with parameter efficiency and overcomes performance bottlenecks in complex mapping scenarios. 3. Experimental Validation: Experimental validation was conducted on the FB15K-237 and WN18 RR benchmarks, including comparative evaluations, statistical significance testing, and ablation studies, to comprehensively assess the performance of the enhanced TransD model. Upon application to our self-constructed California Traffic Accident Knowledge Graph (TAKG), Enhanced TransD achieved optimal performance across all evaluation metrics under medium-to-high embedding dimensions, markedly improving entity ranking and classification accuracy and confirming the synergistic benefits of attribute projection and dual-limit loss. These results demonstrate that the proposed approach possesses strong generalization and discrimination capabilities in multi-source heterogeneous knowledge graph representation learning. By integrating entity-attribute projection vectors and a dual-limit scoring loss into the TransD framework, this work significantly advances the modeling of complex one-to-many, many-to-one, and many-to-many relations, thereby enriching the ecosystem of translation-based knowledge graph embeddings. Compared to prior studies, it introduces fine-grained domain attribute encoding and explicit positive-negative boundary control. The resulting interpretable embeddings can be deployed within intelligent traffic simulation and early-warning systems to enhance the accuracy and responsiveness of accident-risk predictions, providing traffic management authorities with robust, data-driven decision support. Moreover, this research opens several avenues for future innovation. First, the manual selection of entity attributes relies heavily on domain expertise; subsequent work should explore automated attribute screening and the integration of multimodal data.