Urban Expressway Renewal Strategy Based on Knowledge Graphs

Urban Expressway Renewal Strategy Based on Knowledge Graphs

As an important element of urban infrastructure renewal, urban expressway renewal is of great significance to improve the commuting efficiency of cities (especially metropolitan cities), strengthen the service capacity of urban road facilities, and enhance the quality of cities. Considering the adva...

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Bibliographic Details
Published in:Applied sciences 2023-04, Vol.13 (8), p.4666
Main Authors: Zhu, Min, Zhou, Wenbo, Yuan, Tengfei
Format: Article
Language:eng
Subjects:
case similarity
Cities
Commuting
Data models
Decision making
Efficiency
expressway
industry standard
Infrastructure
Infrastructure (Economics)
knowledge graph
knowledge reasoning
Knowledge representation
Maintenance and repair
Ontology
Reasoning
Renewals
Road maintenance
Roads
Roads & highways
Transportation planning
Urban renewal
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Summary:As an important element of urban infrastructure renewal, urban expressway renewal is of great significance to improve the commuting efficiency of cities (especially metropolitan cities), strengthen the service capacity of urban road facilities, and enhance the quality of cities. Considering the advantages of a knowledge graph in the integration of multi-source data and assisted decision-making based on knowledge reasoning, this paper provides assisted decision support for urban expressway renewal with the help of a knowledge graph. In this paper, we sorted out the concepts related to road renewal from road maintenance standards, designed an ontology concept matching algorithm to extract relevant concepts in existing ontologies, constructed concept models, and built a knowledge graph of expressway renewal with ontology as the carrier. Then, based on road maintenance standards and road properties, this work proposes a knowledge reasoning rule combined with case similarity for an expressway renewal strategy. The final experimental results verified the feasibility of the expressway renewal strategy based on the knowledge graph.
ISSN:2076-3417
2076-3417