Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning
Traffic accident data-based portrait plays a vital role in accident cause investigation, relationship reasoning, prevention, and control. The traffic accident data tend to be multisourced with increasingly hidden and complicated accident relationships. The existing reported research focus more on tr...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/8831914 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832543804780969984 |
|---|---|
| author | Chun-Hao Wang Yue-Tian-Si Ji Li Ruan Joshua Luhwago Yin-Xuan Saw Sokhey Kim Tao Ruan Li-Min Xiao Rui-Jue Zhou |
| author_facet | Chun-Hao Wang Yue-Tian-Si Ji Li Ruan Joshua Luhwago Yin-Xuan Saw Sokhey Kim Tao Ruan Li-Min Xiao Rui-Jue Zhou |
| author_sort | Chun-Hao Wang |
| collection | DOAJ |
| description | Traffic accident data-based portrait plays a vital role in accident cause investigation, relationship reasoning, prevention, and control. The traffic accident data tend to be multisourced with increasingly hidden and complicated accident relationships. The existing reported research focus more on traffic drivers’ measurement of penalty, the relationship among drivers, cars, and dates, etc. How to use multisource data based on deep learning, especially based on the Chinese recent unstructured data and structured data to establish accident portrait for individual and groups of accident drivers, still lacks. Moreover, how to perform multisource accident data label extraction, identity, and relationship extraction are still challenging problems. This paper proposes a multisource accident datasets-driven deep learning-based traffic accident portrait method. Our multisource accident datasets-driven deep learning model is composed of the following three submodels: (1) the structured data accident model using our accident feature-driven bidirectional long short-term memory (Bi-LSTM) and accident feature-driven bidirectional conditional random field (Bi-CRF) model to extract labels, (2) the unstructured traffic accident data model using our accident feature-driven piecewise convolutional neural network (PCNN) model to identify the extracted labels, and (3) the semistructured traffic accident data processing model. Moreover, to solve the problem of how to construct hidden relationship among the multisource accident data, a multisource accident data visualization method based on traffic accident knowledge graph where the accident relational inference algorithm is to complete the hidden relationship between traffic accident data labels is used and then data are visualized using the traffic accident knowledge graph. This paper uses the NER dataset of the People’s Daily and a manually labeled dataset to test the Bi-LSTM + Bi-CRF model, and it acquires the highest scores of 0.9562 and 0.9779 compared with several other models. This paper uses the DuIE dataset and a manually labeled dataset to test the PCNN model, and it acquires the highest scores of 0.9674 and 0.9108 compared with several other models. Experiments verified our model’s merits than other models in regards to accident label extraction, accident identity identification, and accident relationship extraction. |
| format | Article |
| id | doaj-art-d032613ca6314231bd345ea47228478d |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-d032613ca6314231bd345ea47228478d2025-02-03T11:30:42ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/8831914Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident ReasoningChun-Hao Wang0Yue-Tian-Si Ji1Li Ruan2Joshua Luhwago3Yin-Xuan Saw4Sokhey Kim5Tao Ruan6Li-Min Xiao7Rui-Jue Zhou8Key Laboratory of Evidence Science (China University of Political Science and Law)State Key Laboratory of Complex and Critical Software EnvironmentIntelligent Connected Vehicle Traffic Accident Investigation and Reconstruction Standard Laboratory of Beijing Municipal Public Security BureauState Key Laboratory of Complex and Critical Software EnvironmentState Key Laboratory of Complex and Critical Software EnvironmentState Key Laboratory of Complex and Critical Software EnvironmentChina Patent Information CenterState Key Laboratory of Complex and Critical Software EnvironmentState Key Laboratory of Complex and Critical Software EnvironmentTraffic accident data-based portrait plays a vital role in accident cause investigation, relationship reasoning, prevention, and control. The traffic accident data tend to be multisourced with increasingly hidden and complicated accident relationships. The existing reported research focus more on traffic drivers’ measurement of penalty, the relationship among drivers, cars, and dates, etc. How to use multisource data based on deep learning, especially based on the Chinese recent unstructured data and structured data to establish accident portrait for individual and groups of accident drivers, still lacks. Moreover, how to perform multisource accident data label extraction, identity, and relationship extraction are still challenging problems. This paper proposes a multisource accident datasets-driven deep learning-based traffic accident portrait method. Our multisource accident datasets-driven deep learning model is composed of the following three submodels: (1) the structured data accident model using our accident feature-driven bidirectional long short-term memory (Bi-LSTM) and accident feature-driven bidirectional conditional random field (Bi-CRF) model to extract labels, (2) the unstructured traffic accident data model using our accident feature-driven piecewise convolutional neural network (PCNN) model to identify the extracted labels, and (3) the semistructured traffic accident data processing model. Moreover, to solve the problem of how to construct hidden relationship among the multisource accident data, a multisource accident data visualization method based on traffic accident knowledge graph where the accident relational inference algorithm is to complete the hidden relationship between traffic accident data labels is used and then data are visualized using the traffic accident knowledge graph. This paper uses the NER dataset of the People’s Daily and a manually labeled dataset to test the Bi-LSTM + Bi-CRF model, and it acquires the highest scores of 0.9562 and 0.9779 compared with several other models. This paper uses the DuIE dataset and a manually labeled dataset to test the PCNN model, and it acquires the highest scores of 0.9674 and 0.9108 compared with several other models. Experiments verified our model’s merits than other models in regards to accident label extraction, accident identity identification, and accident relationship extraction.http://dx.doi.org/10.1155/2024/8831914 |
| spellingShingle | Chun-Hao Wang Yue-Tian-Si Ji Li Ruan Joshua Luhwago Yin-Xuan Saw Sokhey Kim Tao Ruan Li-Min Xiao Rui-Jue Zhou Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning Journal of Advanced Transportation |
| title | Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning |
| title_full | Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning |
| title_fullStr | Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning |
| title_full_unstemmed | Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning |
| title_short | Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning |
| title_sort | multisource accident datasets driven deep learning based traffic accident portrait for accident reasoning |
| url | http://dx.doi.org/10.1155/2024/8831914 |
| work_keys_str_mv | AT chunhaowang multisourceaccidentdatasetsdrivendeeplearningbasedtrafficaccidentportraitforaccidentreasoning AT yuetiansiji multisourceaccidentdatasetsdrivendeeplearningbasedtrafficaccidentportraitforaccidentreasoning AT liruan multisourceaccidentdatasetsdrivendeeplearningbasedtrafficaccidentportraitforaccidentreasoning AT joshualuhwago multisourceaccidentdatasetsdrivendeeplearningbasedtrafficaccidentportraitforaccidentreasoning AT yinxuansaw multisourceaccidentdatasetsdrivendeeplearningbasedtrafficaccidentportraitforaccidentreasoning AT sokheykim multisourceaccidentdatasetsdrivendeeplearningbasedtrafficaccidentportraitforaccidentreasoning AT taoruan multisourceaccidentdatasetsdrivendeeplearningbasedtrafficaccidentportraitforaccidentreasoning AT liminxiao multisourceaccidentdatasetsdrivendeeplearningbasedtrafficaccidentportraitforaccidentreasoning AT ruijuezhou multisourceaccidentdatasetsdrivendeeplearningbasedtrafficaccidentportraitforaccidentreasoning |