Optimal paths planning in dynamic transportation networks with random link travel times

A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers' robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.

Link Prediction Based on the Relational Path Inference of Triangular Structures

Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.

Entity-related paths modeling for knowledge base completion

Knowledge bases(KBs)are far from complete,necessitating a demand for KB completion.Among various methods,embedding has received increasing attention in recent years.PTransE,an important approach using embedding method in KB completion,considers multiple-step relation paths based on TransE,but ignores the association between entity and their related entities with the same direct relationships.In this paper,we propose an approach called EP-TransE,which considers this kind of association.As a matter of fact,the dissimilarity of these related entities should be taken into consideration and it should not exceed a certain threshold.EPTransE adjusts the embedding vector of an entity by comparing it with its related entities which are connected by the same direct relationship.EPTransE further makes the euclidean distance between them less than a certain threshold.Therefore,the embedding vectors of entities are able to contain rich semantic information,which is valuable for KB completion.In experiments,we evaluated our approach on two tasks,including entity prediction and relation prediction.Experimental results show that our idea of considering the dissimilarity of related entities with the same direct relationships is effective.