Recognition and Evaluation on Expressway Traffic Accident Information Using Multimodal Data
In order to extract emergency response information from natural language descriptions of traffic accidents,a named entity recognition method is proposed based on pre-trained models and BiLSTM-CRF.The multimodal traffic accident data on expressways from June 2021 to August 2022 in Shaanxi province are analyzed as data sources.Firstly,3 deep learning models are compared on entity recognition effect and training time.Secondly,the traffic accident corpus from official microblog is obtained to test the robustness.Moreover,according to the dimensions of consistency and richness,the evaluation indicators are constructed to enable quantitative assessment of traffic accident content for text data and structured data.Finally,the traffic accident information recognition is carried out by using the test dataset.The result shows that the weighted Fl values of BERT-BiLSTM-CRF on both test dataset and out-of-bag dataset are 97.029 4％and 69.155 5％respectively,which have the best comprehensive performance in terms of model accuracy,training efficiency,and robustness.It is verified that there is a positive correlation between the number of emergency disposal entities and the duration of accident.The correlation coefficients of disposal agency,disposal equipment,un-disposal,disposal-ing and disposal effect are 0.309,0.151,0.137,0.220 and 0.178 respectively.The content consistency of weather,road loss,traffic diversion,accident type and casualty are 7.06％,45.79％,1.59％,67.65％and 47.59％respectively.The proportion of emergency response is 36％,and the variability is 1.305.It is proved that text data contain rich emergency disposal information,however,the content consistency of text data and structured data for the same traffic accident should be improved.The study result can provide reference for improving the quality and effectiveness of traffic accident information.
ITStraffic accidentmultimodal datapre-trained modelbi-directional long and short term memory
NETL
NSTL
万方数据
