A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model

TANG Jinjun; TUO Haonan; LIU You; FU Qiang

doi:10.3963/j.jssn.1674-4861.2022.05.009

Volume 40 Issue 5

Nov. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Transport Information and Safety > 2022 > 40(5): 80-90

TANG Jinjun, TUO Haonan, LIU You, FU Qiang. A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model[J]. Journal of Transport Information and Safety, 2022, 40(5): 80-90. doi: 10.3963/j.jssn.1674-4861.2022.05.009

Citation:

TANG Jinjun, TUO Haonan, LIU You, FU Qiang. A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model[J]. Journal of Transport Information and Safety, 2022, 40(5): 80-90. doi: 10.3963/j.jssn.1674-4861.2022.05.009

Citation:

PDF( 2072 KB)

A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model

doi: 10.3963/j.jssn.1674-4861.2022.05.009

School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China

Received Date: 2022-01-02
Available Online: 2022-12-05

Abstract

Abstract

Autonomous Transportation System (ATS) consists of participants whose information is generally described by texts. In order to develop a knowledge graph of the participants of the ATS, it is necessary to accurately identify the participants from the texts. Therefore, an entity recognition method based on a BERT-Bi-LSTM-CRF model is developed to extract the participants of ATS. Specifically, a Bi-LSTM (bidirectional long short-term memory) model is used to bi-directionally extract contextual sequence information from the semantic characteristics, which are captured by a word embedding model—BERT (bidirectional encoder representation from transformers). The optimal results of sequence prediction are obtained through the CRF(conditional random fields). After the original text source related to transportation engineering is collected, preprocessed and annotated, a new dataset is developed for identifying the participants of the ATS. Moreover, a comparative experiment of the entity recognition is carried out based on the same dataset. The results indicate that the BERT model significantly improves the performance of identifying the participants. Compared with other methods such as CNN-LSTM and Bi-LSTM, the proposed method achieves the best performance. The overall F1-score of participants is 86.81%, which shows that the proposed BERT model can enhance the generalized capability of the detection methods by extracting the semantic features of participants. The for identifying each type of including "user" "operator" "supplier" "planner" and "maintainer" reaches 90.35%, 92.31%, 90.48%, 93.33%, and 95.00%, respectively. Therefore, it can be concluded from the study results that the proposed method is effective and accurate.
- intelligent transportation,
- named entity recognition,
- knowledge graph,
- BERT-Bi-LSTM-CRF,
- knowledge modeling

FullText(HTML)

References(26)

References

[1]	KHALID M, AWAIS M, SINGH N, et al. Autonomous transportation in emergency healthcare services: Framework, challenges, and future work[J]. IEEE Internet of Things Magazine, 2021, 4(1): 28-33. doi: 10.1109/IOTM.0011.2000076
[2]	魏伟, 郑来, 蔡铭. 面向自主式交通的智能交通系统用户需求研究[J]. 交通科技与经济, 2022, 24(2): 1-7. https://www.cnki.com.cn/Article/CJFDTOTAL-KJJJ202202001.htm WEI W, ZHENG L, CAI M. Research on user needs of Intelligent transportation system for autonomous transportation[J]. Technology & Economy in Areas of Communications, 2022, 24(2): 1-7. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KJJJ202202001.htm
[3]	李瑞敏, 王长君. 智能交通管理系统发展趋势[J]. 清华大学学报(自然科学版), 2022, 62(3): 509-515. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB202203013.htm LI R M, WANG C J. Development of advanced traffic management systems[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(3): 509-515. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB202203013.htm
[4]	程宇航, 张健钦, 李江川, 等. 交通行业事故文本数据的可视化挖掘分析方法[J]. 计算机工程与应用, 2021, 57(21): 116-122. doi: 10.3778/j.issn.1002-8331.2010-0269 CHENG Y H, ZHANG J Q, LI J C, et al. Visual mining and analysis method of text data in traffic accident[J]. Computer Engineering and Applications, 2021, 57(21): 116-122. (in Chinese) doi: 10.3778/j.issn.1002-8331.2010-0269
[5]	刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3): 582-600. https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201603009.htm LIU Q, LI Y, DUAN H, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development, 2016, 53(3): 582-600. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201603009.htm
[6]	王莉. 基于知识图谱的城市轨道交通建设安全管理智能知识支持研究[D]. 徐州: 中国矿业大学, 2019. WANG L. Research on intelligent knowledge support for urban rail transit construction safety management[D]. Xuzhou: China University of Mining and Technology, 2019. (in Chinese)
[7]	ZHANG Q, WEN Y Q, HAN D, et al. Construction of knowledge graph of maritime dangerous goods based on IMDG code[J]. The Journal of Engineering, 2020, 2020(13): 361-365. doi: 10.1049/joe.2019.1147
[8]	刘斐, 贺向阳, 邹志云. 基于全文索引知识图谱的危化品运输地址匹配研究[J]. 计算机应用研究, 2022, 39(2): 407-410+431. doi: 10.19734/j.issn.1001-3695.2021.07.0299 LIU F, HE X Y, ZOU Z Y. Address matching based on full-text indexed knowledge graph for hazardous materials transportation[J]. Application Research of Computers, 2022, 39(2): 407-410+431. (in Chinese) doi: 10.19734/j.issn.1001-3695.2021.07.0299
[9]	LIU J, SCHMID F, LI K, et al. A knowledge graph-based approach for exploring railway operational accidents[J]. Reliability Engineering & System Safety, 2021, (207): 107352.
[10]	奚雪峰, 周国栋. 面向自然语言处理的深度学习研究[J]. 自动化学报, 2016, 42(10): 1445-1465. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201610001.htm XI X F, ZHOU G D. A survey on deep learning for natural language processing[J]. Acta Automatica Sinica, 2016, 42 (10): 1445-1465. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201610001.htm
[11]	焦凯楠, 李欣, 朱容辰. 中文领域命名实体识别综述[J]. 计算机工程与应用, 2021, 57(16): 1-15. doi: 10.3778/j.issn.1002-8331.2103-0127 JIAO K N, LI X, ZHU R C. Overview of Chinese domain named entity recognition[J]. Computer Engineering and Applications, 2021, 57(16): 1-15. (in Chinese) doi: 10.3778/j.issn.1002-8331.2103-0127
[12]	ZULKARNAIN, PUTRI T D. Intelligent Transportation Systems(ITS): A systematic review using a Natural Language Processing(NLP)approach[J]. Heliyon, 2021, 7(12): e08615. doi: 10.1016/j.heliyon.2021.e08615
[13]	孙鑫瑞, 孟雨, 王文乐. 基于知识图谱与目标检测的微博交通事件识别[J]. 数据分析与知识发现, 2020, 4(12): 136-147. https://www.cnki.com.cn/Article/CJFDTOTAL-XDTQ202012018.htm SUN X R, MENG Y, WANG W L. Identifying traffic events from Weibo with knowledge graph and target detection[J]. Data Analysis and Knowledge Discovery, 2020, 4(12): 136-147. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XDTQ202012018.htm
[14]	郑治豪, 吴文兵, 陈鑫, 等. 基于社交媒体大数据的交通感知分析系统[J]. 自动化学报, 2018, 44(4): 656-666. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201804007.htm ZHENG Z H, WU W B, CHEN X, et al. A traffic sensing and analyzing system using social media data[J]. Acta Automatica Sinica, 2018, 44(4): 656-666. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201804007.htm
[15]	孙欢. 基于BERT+BiLSTM+CRF模型和改进Apriori算法的交通事故文本分析[D]. 西安: 长安大学, 2021. SUN H. Traffic accident text analysis based on BERT+BiLSTM + CRF model and improved Apriori algorithm[D]. Xi'an: Chang'an University, 2021. (in Chinese)
[16]	李韧, 李童, 杨建喜, 等. 基于Transformer-BiLSTM-CRF的桥梁检测领域命名实体识别[J]. 中文信息学报, 2021, 35 (4): 83-91. https://www.cnki.com.cn/Article/CJFDTOTAL-MESS202104012.htm LI R, LI T, YANG J X, et al. Bridge inspection named entity recognition based on Transformer-BiLSTM-CRF[J]. Journal of Chinese Information Processing, 2021, 35(4): 83-91. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MESS202104012.htm
[17]	XU Q, LI K Q, WANG J Q, et al. The status, challenges, and trends: An interpretation of technology roadmap of intelligent and connected vehicles in China(2020)[J]. Journal of Intelligent and Connected Vehicles, 2022, 5(1): 1-7.
[18]	CHEN H L, ZHU M, WEN Y Q, et al. An implementable architecture of inland autonomous waterway transportation system[J]. IFAC-PapersOnLine, 2021, 54(16): 37-42.
[19]	张雷, 沈国琛, 秦晓洁, 等. 智能网联交通系统中的信息物理映射与系统构建[J]. 同济大学学报(自然科学版), 2022, 50(1): 79-86. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ202201009.htm ZHANG L, SHEN G C, QIN X J, et al. Information physical mapping and system construction of intelligent network transportation[J]. Journal of Tongji University(Natural Science), 2022, 50(1): 79-86. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ202201009.htm
[20]	DE OLIVEIRA K M, BACHA F, MNASSER H, et al. Transportation ontology definition and application for the content personalization of user interfaces[J]. Expert Systems with Applications, 2013, 40(8): 3145-3159.
[21]	GREGOR D, TORAL S, ARIZA T, et al. A methodology for structured ontology construction applied to intelligent transportation systems[J]. Computer Standards & Interfaces, 2016 (47): 108-119.
[22]	田玲, 张谨川, 张晋豪, 等. 知识图谱综述: 表示、构建、推理与知识超图理论[J]. 计算机应用, 2021, 41(8): 2161-2186. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY202108002.htm TIAN L, ZHANG J C, ZHANG J H, et al. Knowledge graph survey: Representation, construction, reasoning and knowledge hypergraph theory[J]. Journal of Computer Applications, 2021, 41(8): 2161-2186. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY202108002.htm
[23]	MAHMOOD T, MUJTABA G, SHUIB L, et al. Public bus commuter assistance through the named entity recognition of twitter feeds and intelligent route finding[J]. IET Intelligent Transport Systems, 2017, 11(8): 521-529.
[24]	SUAT-ROJAS N, GUTIERREZ-OSORIO C, PEDRAZA C. Extraction and analysis of social networks data to detect traffic accidents[J]. Information, 2022, 13(1): 26.
[25]	刘昭, 何赏璐, 刘英舜. 基于社交网络数据的交通突发事件识别方法[J]. 交通信息与安全, 2021, 39(2): 53-60. doi: 10.3963/j.jssn.1674-4861.2021.02.007 LIU Z, HE S L, LIU Y S. A method to identify traffic incidents based on social network data[J]. Journal of Transport Information and Safety, 2021, 39(2): 53-60. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.02.007
[26]	VIJAYAKUMAR V, VAIRAVASUNDARAM S, LOGESH R, et al. Effective knowledge based recommender system for tailored multiple point of interest recommendation[J]. International Journal of Web Portals, 2019, 11(1): 1-18.

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(7) / Tables(6)

Get Citation

PDF

XML

Article Metrics

Article views (976) PDF downloads(38)

A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model

doi: 10.3963/j.jssn.1674-4861.2022.05.009

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model

doi: 10.3963/j.jssn.1674-4861.2022.05.009

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content