Development of a Knowledge Base for Reasoning Penalty for Traffic Violations Based on Event Evolutionary Graph

WANG Cui; HU Haotian; DENG Sanhong

doi:10.3963/j.jssn.1674-4861.2022.01.005

Volume 40 Issue 1

Feb. 2022

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Article Navigation > Journal of Transport Information and Safety > 2022 > 40(1): 36-44

WANG Cui, HU Haotian, DENG Sanhong. Development of a Knowledge Base for Reasoning Penalty for Traffic Violations Based on Event Evolutionary Graph[J]. Journal of Transport Information and Safety, 2022, 40(1): 36-44. doi: 10.3963/j.jssn.1674-4861.2022.01.005

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WANG Cui, HU Haotian, DENG Sanhong. Development of a Knowledge Base for Reasoning Penalty for Traffic Violations Based on Event Evolutionary Graph[J]. Journal of Transport Information and Safety, 2022, 40(1): 36-44. doi: 10.3963/j.jssn.1674-4861.2022.01.005

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Development of a Knowledge Base for Reasoning Penalty for Traffic Violations Based on Event Evolutionary Graph

doi: 10.3963/j.jssn.1674-4861.2022.01.005

WANG Cui^{1, 2
,},
HU Haotian^{1, 2},
DENG Sanhong^{1, 2
,
,}

1.
School of Information Management, Nanjing University, Nanjing 210023, China
2.
Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing University, Nanjing 210023, China

Received Date: 2021-06-15
Available Online: 2022-03-31

Abstract

Abstract

With continuous improvement of laws and regulations related to road safety in China, traffic police departments are required to issue different penalties for specific traffic violations. In response to the call and to improve the capacity of"intelligent governance", this article proposes to develop a knowledge base for traffic violations and accidents with event evolutionary graph, which can be used to reason appropriate penalty for traffic violations& accidents quickly and efficiently. This paper uses open-source data to develop the knowledge base required for processing traffic violations/accidents and creates an event evolutionary graph through extracting traffic events and their relationship. Moreover, a knowledge base system for traffic violations/accidents is developed. The experimental results show that the proposed system offers a F1 score of 0.832 when classifying traffic violations and accidents, which indicates that the event evolutionary graph is a good tool for reasoning the penalty of traffic violations and accidents.
- traffic safety,
- traffic violation,
- event evolutionary graph,
- knowledge base system,
- road traffic

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References(27)

References

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Development of a Knowledge Base for Reasoning Penalty for Traffic Violations Based on Event Evolutionary Graph

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