A Method to Identify Traffic Incidents Based on Social Network Data

LIU Zhao; HE Shanglu; LIU Yingshun

doi:10.3963/j.jssn.1674-4861.2021.02.007

Volume 39 Issue 2

Apr. 2021

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Article Navigation > Journal of Transport Information and Safety > 2021 > 39(2): 53-60

LIU Zhao, HE Shanglu, LIU Yingshun. A Method to Identify Traffic Incidents Based on Social Network Data[J]. Journal of Transport Information and Safety, 2021, 39(2): 53-60. doi: 10.3963/j.jssn.1674-4861.2021.02.007

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LIU Zhao, HE Shanglu, LIU Yingshun. A Method to Identify Traffic Incidents Based on Social Network Data[J]. Journal of Transport Information and Safety, 2021, 39(2): 53-60. doi: 10.3963/j.jssn.1674-4861.2021.02.007

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A Method to Identify Traffic Incidents Based on Social Network Data

doi: 10.3963/j.jssn.1674-4861.2021.02.007

School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

Received Date: 2020-06-18

Abstract

Abstract

A text classification method based on machine learning is studied to identify traffic incidents by mining the data from the social networks. The original texts are crawled by web crawler"Beautiful Soup"based on the keywords and location. These texts are preprocessed using regular expression matching, duplicate removing, and"0-1"mark? ing. According to the features of preprocessed texts, the paper proposes a method to select feature words based on fea? ture weights. The feature weight is calculated by normalizing, weighting, and combining the word frequency and the ratio of the text containing that word. Accordingly, the feature weight of each unique word in the training set of the traf? fic incident text can be achieved, used as a criterion for selecting feature words, and as the inputs of classifiers. A test is conducted to compare different classifiers and methods to select feature words. The results show that the proposed method to select feature words combined with the XGBoost classifier has the optimal performance, with a precision rate of 0.679 6, a recall rate of 0.648 1, an F1 value of 0.663 5, and an AUC value of 0.759 4.
- intelligent transportation,
- social network data,
- traffic incident identification,
- text classification,
- machine learning

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References(19)

References

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