A search and summary application for traffic events detection based on Twitter data
A search and summary application for traffic events detection based on Twitter data
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DOI：
10.1145/2666310.2666366
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
Meiling Liu;Kaiqun Fu;Chang-Tien Lu;Guangsheng Chen;Huiqiang Wang
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文献类型：
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作者：
Meiling Liu;Kaiqun Fu;Chang-Tien Lu;Guangsheng Chen;Huiqiang Wang
作为社交媒体的一种形式，Twitter记录了我们城市发生的现实生活事件。每天发表大量在运输或地铁标题下的推文。本文根据在异常时发布的推文中的采矿代表术语介绍了交通事件检测和摘要（TEDS）的申请。所提出的集合应用程序包含具有多个索引，排名和评分方案的有效TEDS搜索引擎。时空分析和新型小波分析模型用于交通事件检测。该应用程序可以使驾驶员和运输当局受益。用户可以搜索运输状态并分析感兴趣的特定位置的交通事件。利用提出的信号处理技术，我们通过检查华盛顿特区地区的交通和地铁旅行来证明该系统的有效性。随着公民生活与社交媒体之间的合作变得越来越大，这可能会对交通流量，旅行选择和其他城市计算功能的预测产生重大影响。
As a form of social media, Twitter records real life events in our cities as they happen. Huge numbers of tweets under the heading of transportation or metro are published every day. This paper presents an application for Traffic Events Detection and Summary (TEDS) based on mining representative terms from the tweets posted when anomalies occur. The proposed ensemble application contains an efficient TEDS search engine with multiple indexing, ranking, and scoring schemes. Spatio-temporal analysis and a novel wavelet analysis model are applied for traffic event detection. This application could benefit both drivers and transportation authorities. Users can search transportation status and analyze traffic events in specific locations of interest. Utilizing the proposed signal processing technology, we demonstrate the system's effectiveness by examining traffic and metro travel in the Washington D.C. area. As the collaboration between a citizen's life and social media becomes ever greater, this could have a significant impact on the prediction of traffic flow, travel selection, and other city computing functions.