Abstract
The integration of multi-source transportation data is complex and insufficient in most of the big cities, which made it difficult for researchers to conduct in-depth data mining to improve the policy or the management. In order to solve this problem, a top-down approach is used to construct a knowledge graph of urban traffic system in this paper. First, the model layer of the knowledge graph was used to realize the reuse and sharing of knowledge. Furthermore, the model layer then was stored in the graph database Neo4j. Second, the representation learning based knowledge reasoning model was adopted to implement knowledge completion and improve the knowledge graph. Finally, the proposed method was validated with an urban traffic data set and the results showed that the model could be used to mine the implicit relationship between traffic entities and discover traffic knowledge effectively.
Highlights
The data used in this paper were the data of a public traffic knowledge graph stored in the graph database
This paper studied the construction method of a knowledge graph in the field of urban traffic
The extracted entities, attributes, and relationships between entities were stored in the graph database Neo4j, and the construction of a public traffic knowledge graph and an urban road traffic travel knowledge graph was completed
Summary
With the rapid development of information technology, such as Big Data, cloud computing, artificial intelligence, and the Internet of Things, various terminals have generated massive traffic data, such as mobile phone signaling and public traffic card swiping data containing travel records and vehicle information obtained by video surveillance equipment on the road. Traffic Big Data have the characteristics of large quantity, variety, wide coverage, fragmentation, and so on [1]. A knowledge graph describes the various concepts, entities, and relationships between entities in the objective world in a structured way, and provides a better ability to organize, manage, and understand massive amounts of information [2]. There are already some vertical knowledge graphs for specific fields, but data sparseness still exists. The kind of reasoning method to be used to solve the problem of completeness of a knowledge graph is the current research hotspot in the field of knowledge graphs
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