Survey of Dynamic Knowledge Graph for Urban Traffic: Construction, Representation and Application

被引：0
作者：
Liu Y. [1 ]
Ning N. [1 ]
Yang D. [2 ]
Li W. [1 ]
Wu B. [3 ]
Zhou Y. [1 ]
机构：
[1] School of Artificial Intelligence, Henan University, Zhengzhou
[2] Mobile Ecology Business Group Search Strategy Department, Baidu Netcom Science and Technology Co., Ltd., Beijing
[3] School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
来源：
Journal of Geo-Information Science | 2024年 / 26卷 / 04期
基金：
中国国家自然科学基金;
关键词：
assisted decision making; graph neural network; intelligent transportation; knowledge graph; knowledge reasoning; knowledge representation; traffic management; urban planning;
D O I：
10.12082/dqxxkx.2024.230572
中图分类号：
学科分类号：
摘要：
In the field of intelligent transportation, various information collection devices have produced a massive amount of multi-source heterogeneous data. These data encompass various types of information, including vehicle trajectories, road conditions, and traffic incidents, soured from devices such as traffic cameras, sensors, and GPS. However, the current challenge faced by researchers and practitioners is how to correlate and integrate the massive amount of heterogeneous data to facilitate decision support. To address this challenge, knowledge graph technology, with its powerful entity-to-entity modeling ability, has shown great potential in knowledge mining, representation, management, and reasoning, making it well-suited for intelligent transportation applications. In this paper, we first review the construction techniques for geographic traffic graphs, multimodal knowledge graphs, and dynamic knowledge graphs, demonstrating the broad applicability of knowledge graphs in the field of intelligent transportation. Secondly, we summarize relevant algorithms of multi-modal knowledge graph representation learning and discuss dynamic knowledge graph representation learning in the field of intelligent transportation. Knowledge graph representation learning technology plays a crucial role in creating high-quality knowledge graphs by capturing and organizing the relationships between entities and their attributes within the transportation domain. This technology utilizes advanced machine learning algorithms to analyze and process the heterogeneous data from various sources to extract meaningful patterns and structures. We also introduce the completion technology and causal reasoning technology in dynamic transportation multi- modal knowledge graph, which is useful for improving the data of intelligent transportation systems. Comprehension ability and decision-making reasoning level have important theoretical significance and practical application prospects. Thirdly, we summarize the solutions of knowledge graph that provide important support for intelligent decision-making in several application scenarios. The utilization of knowledge graphs in intelligent transportation systems facilitates real-time data integration and enables correlation analysis of diverse data sources to provide a holistic view of the traffic ecosystem. This comprehensive understanding empowers decision-makers to implement targeted interventions and proactive measures, ultimately mitigating traffic congestion and reducing the occurrence of accidents. Through the continuous refinement and enrichment of the traffic knowledge graph, the intelligent transportation system can adapt and evolve to address emerging challenges and optimize transport networks for enhanced efficiency and safety. Finally, we analyze and discuss the existing technical bottlenecks. The future of traffic knowledge graphs and their auxiliary applications are also prospected and discussed, highlighting the potential impact of this important technology on intelligent transportation systems. © 2024 Science Press. All rights reserved.
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页码：946 / 966
页数：20
相关论文
共 89 条
  • [1] Zhu L., Yu F.R., Wang Y.G., Et al., Big data analytics in intelligent transportation systems: A survey[J], IEEE Transactions on Intelligent Transportation Systems, 20, 1, pp. 383-398, (2019)
  • [2] Liu J., Li T.R., Ji S.G., Et al., Urban flow pattern mining based on multi- source heterogeneous data fusion and knowledge graph embedding[J], IEEE Transactions on Knowledge and Data Engineering, 35, 2, pp. 2133-2146, (2023)
  • [3] Wang W.Y., Siau K., Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity[J], Journal of Database Management, 30, 1, pp. 61-79, (2019)
  • [4] Liu J.N., Liu H.Y., Chen X.H., Et al., Construction of knowledge graph based on geo-spatial data[J], Journal of Chinese Information Processing, 34, 11, pp. 29-36, (2020)
  • [5] Lu F., Zhu Y.Q., Zhang X.Y., Spatiotemporal knowledge graph: Advances and perspectives[J], Journal of Geo- information Science, 25, 6, pp. 1091-1105, (2023)
  • [6] Zhou Y.C., Wang W.J., Qiao Z.Y., Et al., A survey on the construction methods and applications of sci-tech big data knowledge graph[J], Scientia Sinica Informationis, 50, 7, pp. 957-987, (2020)
  • [7] Jiang X.H., Shen Y.H., Li Z.J., Et al., A survey of social knowledge graph[J], Chinese Journal of Computers, 46, 2, pp. 304-330, (2023)
  • [8] Ji S.X., Pan S.R., Cambria E., Et al., A survey on knowledge graphs: Representation, acquisition, and applications[J], IEEE Transactions on Neural Networks and Learning Systems, 33, 2, pp. 494-514, (2022)
  • [9] Wang M., Wang H.F., Li B.H., Et al., Survey on key technologies of new generation knowledge graph[J], Journal of Computer Research and Development, 59, 9, pp. 1947-1965, (2022)
  • [10] Sun S.F., Li X.L., Li W.S., Et al., Review of graph neural networks applied to knowledge graph reasoning[J], Journal of Frontiers of Computer Science & Technology, 17, 1, pp. 27-52, (2023)
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