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基于社交媒体大数据的交通感知分析系统

郑治豪 吴文兵 陈鑫 胡荣鑫 柳鑫 王璞

郑治豪, 吴文兵, 陈鑫, 胡荣鑫, 柳鑫, 王璞. 基于社交媒体大数据的交通感知分析系统. 自动化学报, 2018, 44(4): 656-666. doi: 10.16383/j.aas.2017.c160537
引用本文: 郑治豪, 吴文兵, 陈鑫, 胡荣鑫, 柳鑫, 王璞. 基于社交媒体大数据的交通感知分析系统. 自动化学报, 2018, 44(4): 656-666. doi: 10.16383/j.aas.2017.c160537
ZHENG Zhi-Hao, WU Wen-Bing, CHEN Xin, HU Rong-Xin, LIU Xin, WANG Pu. A Traffic Sensing and Analyzing System Using Social Media Data. ACTA AUTOMATICA SINICA, 2018, 44(4): 656-666. doi: 10.16383/j.aas.2017.c160537
Citation: ZHENG Zhi-Hao, WU Wen-Bing, CHEN Xin, HU Rong-Xin, LIU Xin, WANG Pu. A Traffic Sensing and Analyzing System Using Social Media Data. ACTA AUTOMATICA SINICA, 2018, 44(4): 656-666. doi: 10.16383/j.aas.2017.c160537

基于社交媒体大数据的交通感知分析系统

doi: 10.16383/j.aas.2017.c160537
基金项目: 

国家自然科学基金面上项目 61473320

中南大学创新驱动计划项目 2016CSX014

湖南省科技计划项目 2015RS4011

详细信息
    作者简介:

    郑治豪  中南大学交通运输工程学院本科生.主要研究方向为交通大数据.E-mail:vincentzheng@csu.edu.cn

    吴文兵  中南大学软件学院本科生.主要研究方向为机器学习.E-mail:SoundsOfLife@163.com

    陈鑫  中南大学信息科学与工程学院本科生.主要研究方向为网络大数据挖掘与分析.E-mail:1774885528@qq.com

    胡荣鑫  中南大学交通运输工程学院本科生.主要研究方向为物流与电子商务.E-mail:hurongxin@csu.edu.cn

    柳鑫  中南大学交通运输工程学院本科生.主要研究方向为城市公共交通规划、运营与管理.E-mail:1104130901@csu.edu.cn

    通讯作者:

    王璞  中南大学交通运输工程学院教授.2010年5月在美国圣母大学获得博士学位, 2010~2011年于美国麻省理工学院进行博士后研究工作.主要研究方向为交通大数据, 社会交通, 复杂网络.担任IEEE Transactions on Intelligent Transportation Systems副主编, IEEE智能交通系统学会-社会交通系统技术委员会Co-Chair.本文通信作者.E-mail:wangpu@csu.edu.cn

A Traffic Sensing and Analyzing System Using Social Media Data

Funds: 

National Natural Science Foundation of China 61473320

Innovation Driven Plan of Central South University 2016CSX014

Science and Technology Project of Hunan Province 2015RS4011

More Information
    Author Bio:

      Undergraduate at the School of Traffic and Transportation Engineering, Central South University. His research interest covers transportation big data analysis

      Undergraduate at the School of Software, Central South University. His main research interest is machine learning

      Undergraduate at the School of Information and Science and Engineering, Central South University. His research interest covers network big data mining and analysis

      Undergraduate at the School of Traffic and Transportation Engineering, Central South University. His research interest covers logistics and e-commerce

      Undergraduate at the School of Traffic and Transportation Engineering, Central South University. His research interest covers urban public transport planning, operation, and management

    Corresponding author: WANG Pu   Professor at the School of Traffic and Transportation Engineering in Central South University. He received his Ph. D. degree in Physics from University of Notre Dame in 2010. From 2010 to 2011, he worked as a postdoctor researcher in the Department of Civil and Environmental Engineering in MIT. His research interest covers transportation big data, social transportation and complex networks. He is an associate editor of IEEE Transactions on Intelligent Transportation Systems and the Co-Chair of IEEE ITSS Social Transportation Systems Technical Committee. Corresponding author of this paper
  • 摘要: 社交媒体数据中蕴含了丰富的交通状态信息,这些信息以人类语言为载体,包含了大量对交通状态的因果分析与多角度描述,可以为传统交通信息采集手段提供有力补充,近年来已成为交通状态感知的重要信息来源.本文以新浪微博为主要数据来源,分别利用支持向量机算法、条件随机场算法以及事件提取模型完成微博的分类、命名实体识别与交通事件提取,开发了基于社交媒体大数据的交通感知分析与可视化系统,可以为交通管理部门及时提供交通舆情及突发交通事件的态势、影响范围、起因等信息.在交通信息采集系统建设较为薄弱的地区,本文建立的系统可以为交通管理提供信息补充.
    1)  本文责任编委 王飞跃
  • 图  1  系统构架图

    Fig.  1  Architecture of the system

    图  2  文本向量化流程图

    Fig.  2  Flowchart of document vectorization

    图  3  时间实体与地点实体示例

    Fig.  3  An example of time entity and location entity

    图  4  命名实体标注示例

    Fig.  4  Examples of NER labels

    图  5  微博命名实体标注结果

    Fig.  5  Weibo NER labelling results

    图  6  可视化模块

    Fig.  6  Visualization module

    图  7  13:55系统在相关路段的监测截图

    Fig.  7  A system screenshot at 13:55

    图  8  偏差数据示例

    Fig.  8  An example of bias

    表  1  关键词表

    Table  1  Keywords list

    车祸剐蹭事故绕行
    追尾相撞塞车高速
    下载: 导出CSV

    表  2  标准化微博数据

    Table  2  Standardized Weibo data

    微博发布时间官方标记微博正文微博定位地点(缺省为*)
    2016040220420竟然能在一个地方堵车堵快1个小时了!气得好多人中途下车了!北京·北七家
    下载: 导出CSV

    表  3  不同分类算法的测试结果

    Table  3  Test results of different algorithms

    算法PrecisionRecallF1-score
    SVM (kernel = 'linear')0.8800.8500.859
    SVM (kernel = 'rbf')0.7470.5740.504
    SVM (kernel = 'sigmoid')0.7990.5240.419
    SVM (kernel = 'poly')0.2340.5000.318
    1NN0.6930.6850.683
    3NN0.7250.6990.692
    5NN0.72707170.717
    Gaussian NB0.6450.6260.618
    Multinomial NB0.7660.7680.767
    DT (criterion = 'entropy')0.6760.6870.676
    DT (criterion = 'gini')0.6740.6770.672
    下载: 导出CSV

    表  4  微博的词序列示例

    Table  4  An example of a sequence of Weibo word

    微博词序列示例词性符号词性
    1月ntnttemporal noun
    6日nt
    13:55mmnumber
    , wp
    jwppunctuation
    j
    高速djabbreviation
    v
    渝段ndadverb
    上行v
    方向nvverb
    白市驿ns
    pngeneral noun
    中梁山ns
    隧道nnsgeographical name
    车流量n
    appreposition
    下载: 导出CSV

    表  5  命名实体标注方案

    Table  5  Method of NER labelling

    类别标注符号说明词序列示例标注示例
    B-Ns地点词的起始1月ntB-Nm
    6日ntI-Nm
    I-Ns地点词的中部13:55mE-Nm
    wpB-Ns
    E-Ns地点词的结尾jI-Ns
    高速jI-Ns
    S-Ns完整的地点词dI-Ns
    渝段vE-Ns
    B-Nm时间词的起始上行nO
    方向vO
    I-Nm时间词的中部白市驿nS-Ns
    nsO
    E-Nm时间词的结尾中梁山ntB-Ns
    隧道nE-Ns
    S-Nm完整的时间词车流量nO
    wpO
    下载: 导出CSV

    表  6  CRF不同模板的设置方案与测试结果

    Table  6  Settings of different CRF templates and test results

    方案窗口大小考虑的列考虑的相对关系PrecisionRecallF1-score
    3aN/A0.7900.6650.72
    3a, bN/A0.7980.7430.769
    3a, ba, b0.7940.7540.773
    5aN/A0.7870.6390.703
    5a, bN/A0.7880.7350.760
    5a, ba, b0.7910.7410.764
    下载: 导出CSV

    表  7  交通事件归类

    Table  7  Classification of traffic events

    路况正常施工封路
    路况拥堵车辆相撞其他
    下载: 导出CSV
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  • 收稿日期:  2016-07-19
  • 录用日期:  2017-04-07
  • 刊出日期:  2018-04-20

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