基于社交网络数据的交通突发事件识别方法

刘昭; 何赏璐; 刘英舜

doi:10.3963/j.jssn.1674-4861.2021.02.007

基于社交网络数据的交通突发事件识别方法

doi: 10.3963/j.jssn.1674-4861.2021.02.007

南京理工大学自动化学院南京 210094

基金项目:

江苏省自然科学基金项目 BK20180486

中国博士后科学基金项目 2018M642257

中央高校基本科研业务费专项资金 30920021140

详细信息

作者简介:
刘昭(1995—)，硕士研究生.研究方向：交通信息工程及控制.Email: 1486729439@qq.com

通讯作者:
何赏璐(1987—)，博士，讲师.研究方向：智能网联交通系统.E-mail：slhemickey@126.com

中图分类号: U492.8
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出版历程
- 收稿日期: 2020-06-18

A Method to Identify Traffic Incidents Based on Social Network Data

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

摘要

摘要: 为了从社交网络数据中挖掘出交通突发事件，研究了基于机器学习的文本识别方法。通过关键词和地点定位，利用网页爬虫“Beautiful Soup”爬取到原始文本。采用正则匹配、重复度计算以及“0-1”标记预处理原始文本。基于预处理后文本特征，研究了基于特征权重的特征词选取方法；其中，特征权重的计算综合了词语的出现频率和含有该词语的文本所占比例，通过将二者归一化并加权合并，获得训练集突发事件文本中各个无重复词语的特征权重；依据此值选择确定特征词，并用于后续分类器的输入。测试对比了不同的分类器以及特征词选择方法，结果表明，所提特征词选取方法与XGBoost分类器结合，在交通突发事件识别上具有最好的综合表现，精确率为0.679 6，召回率为0.648 1，F1值为0.663 5，AUC值为0.759 4。
- 智能交通 /
- 社交网络数据 /
- 交通突发事件识别 /
- 文本分类 /
- 机器学习
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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图 1 研究流程图

Figure 1. Flow for the study

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图 2 样本集预处理流程图

Figure 2. Flow of preprocessing the sample set

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图 3 2019年交通突发事件文本数量

Figure 3. Number of texts on traffic emergencies in 2019

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图 4 2020年交通突发事件文本数量

Figure 4. Number of texts on traffic emergencies in 2020

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图 5 不同特征词数量下的指标对比

Figure 5. Comparison of indices under different eigenvalues

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图 6 词云图

Figure 6. Word cloud

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图 7 交通事故识别与官方报道对比

Figure 7. Comparison between traffic accident identification and official reports

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表 1 样本“0-1”标记示例

Table 1. Cases of "0-1" labeled samples

标记	文本样本
0	高速公路的设计，忌讳一直都笔直向前，哪怕是大平原上，危险容易造成事故
1	@江苏交警警察同志，请问，在图 1沪宁高速路段发生交通事故，一些车辆(图 2-7)可以“借用”应急车道通行吗如果可以，下次，我们都这样做！

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表 2 分词和过滤停用词的结果示例

Table 2. A case for segmenting words and filtering stop words

原文本	@江苏交警警察同志，请问，在图 1沪宁高速路段发生交通事故，一些车辆(图 2〜7)可以“借用”应急车道通行吗？如果可以，下次，我们都这样做！
分词和过滤停用词处理后的文本	江苏/交警/警察/同志/请问/图/I/沪宁/高速/路段/发生/交通/事故/车辆/图/27/借用/应急/车道/通行/下次/都/做/

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表 3 精确率对比

Table 3. Comparison of precision rates

	KNN	SVM	AdaBoost	XGBoost
新方法	0.583 9	0.647 4	0.720 5	0.679 6
TF-IDF	0.528 2	0.730 2	0.640 4	0.594 2
CHI	0.655 2	0.646 7	0.655 4	0.625 8
LDA	0.397 0	0.527 3	0.402 9	0.455 1

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表 4 召回率对比

Table 4. Comparison of recall rates

	KNN	SVM	AdaBoost	XGBoost
新方法	0.370 4	0.518 5	0.537 0	0.648 1
TF-IDF	0.347 2	0.425 9	0.601 9	0.569 4
CHI	0.263 9	0.449 1	0.537 0	0.472 2
LDA	0.365 7	0.268 5	0.384 3	0.375 0

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表 5 F1值对比

Table 5. Comparison of F1 values

	KNN	SVM	AdaBoost	XGBoost
新方法	0.453 3	0.575 8	0.615 4	0.663 5
TF-IDF	0.419 0	0.538 0	0.620 5	0.581 6
CHI	0.376 2	0.530 1	0.590 3	0.538 3
LDA	0.380 7	0.355 8	0.393 4	0.411 2

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表 6 AUC值对比

Table 6. Comparison of AUC values

	KNN	SVM	AdaBoost	XGBoost
新方法	0.629 3	0.699 5	0.724 4	0.759 4
TF-IDF	0.607 9	0.679 6	0.729 4	0.702 3
CHI	0.602 5	0.672 6	0.708 7	0.676 3
LDA	0.565 2	0.583 3	0.571 5	0.592 4

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表 7 参数设置

Table 7. Parameter settings

特征词选择	W	0.5
特征词选择	特征词数量	150
XGBoost	max_depth	5
	booster	gbtree
	objective	binary: logistic
	scale_pos_weight	3
	min_child_weight	1
	learning_rate	0.1
	n_estimators	100

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