Difference between revisions of "Traffic"
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| + | ==Traffic Management== | ||
| − | ==Extracting City Traffic Events from Social Streams== | + | ==Problem of Understanding City Traffic Events== |
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| + | ==City as a Physical-Cyber-Social System== | ||
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| + | ==Solution== | ||
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| + | ==Event Extraction== | ||
| + | ===Extracting City Traffic Events from Social Streams=== | ||
Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events. | Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events. | ||
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We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework. | We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework. | ||
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| + | ==Event Understanding== | ||
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| + | ==Action Recommendation== | ||
Revision as of 14:21, 8 January 2015
Contents
Traffic Management
Problem of Understanding City Traffic Events
City as a Physical-Cyber-Social System
Solution
Event Extraction
Extracting City Traffic Events from Social Streams
Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events.
We use Open Science Framework to share project resources with the research community. Here is the link for our project on Open Science Framework.
