Abstract: Shortest path queries on temporal graphs have significant applications in various fields such as online social network analysis, urban traffic route planning, and communication network analysis. However, existing algorithms for finding shortest paths in temporal graphs mostly adopt a point-by-point or edge-by-edge expansion approach, which results in low computational efficiency. To address this issue, this paper proposes a method for temporal graph shortest path queries based on Algebraic Decision Diagrams (ADD). The temporal graph is first transformed into an ADD structure. Then, leveraging the information in the ADD structure, four different algorithms for temporal shortest path queries under various path definitions, termed TP-DD is designed. These algorithms accelerate the query process by performing set operations to process edges in the temporal graph in parallel. Experimental results on four real-world datasets demonstrate that the proposed algorithms outperform existing methods in terms of query performance.