Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model
文献类型：期刊论文
| 作者 | Lu, Hao1,2 ; Shi, Kaize1; Zhu, Yifan1; Lv, Yisheng2; Niu, Zhendong1
|
| 刊名 | SENSORS
 |
| 出版日期 | 2018-12-01 |
| 卷号 | 18期号:12页码:22 |
| 关键词 | intelligent sensors social transportation multi-channel signals event detection word2vec-based event fusion |
| ISSN号 | 1424-8220 |
| DOI | 10.3390/s18124093 |
| 通讯作者 | Lv, Yisheng(yisheng.lv@ia.ac.cn) ; Niu, Zhendong(zniu@bit.edu.cn) |
| 英文摘要 | Social sensors perceive the real world through social media and online web services, which have the advantages of low cost and large coverage over traditional physical sensors. In intelligent transportation researches, sensing and analyzing such social signals provide a new path to monitor, control and optimize transportation systems. However, current research is largely focused on using single channel online social signals to extract and sense traffic information. Clearly, sensing and exploiting multi-channel social signals could effectively provide deeper understanding of traffic incidents. In this paper, we utilize cross-platform online data, i.e., Sina Weibo and News, as multi-channel social signals, then we propose a word2vec-based event fusion (WBEF) model for sensing, detecting, representing, linking and fusing urban traffic incidents. Thus, each traffic incident can be comprehensively described from multiple aspects, and finally the whole picture of unban traffic events can be obtained and visualized. The proposed WBEF architecture was trained by about 1.15 million multi-channel online data from Qingdao (a coastal city in China), and the experiments show our method surpasses the baseline model, achieving an 88.1% F-1 score in urban traffic incident detection. The model also demonstrates its effectiveness in the open scenario test. |
| WOS关键词 | SENTIMENT ANALYSIS ; TRAFFIC CONGESTION ; TWITTER ; SYSTEMS ; MEDIA ; WEB |
| 资助项目 | National Natural Science Foundation of China[61233001] ; National Natural Science Foundation of China[61773381] ; National Natural Science Foundation of China[61370137] ; Ministry of Education-China Mobile Research Foundation[2016/2-7] |
| WOS研究方向 | Chemistry ; Electrochemistry ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000454817100012 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Ministry of Education-China Mobile Research Foundation |
| 源URL | [http://ir.ia.ac.cn/handle/173211/25305]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室 |
| 通讯作者 | Lv, Yisheng; Niu, Zhendong |
| 作者单位 | 1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Hao,Shi, Kaize,Zhu, Yifan,et al. Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model[J]. SENSORS,2018,18(12):22. |
| APA | Lu, Hao,Shi, Kaize,Zhu, Yifan,Lv, Yisheng,&Niu, Zhendong.(2018).Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model.SENSORS,18(12),22. |
| MLA | Lu, Hao,et al."Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model".SENSORS 18.12(2018):22. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。