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2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS) (2015)
Sydney, Australia
Dec. 11, 2015 to Dec. 13, 2015
ISBN: 978-1-5090-0214-6
pp: 17-24
ABSTRACT
Twitter, the popular microblogging platform, has more than five hundred million registered users -- so called Tweeters. These Tweeters generate a large amount of information every day. This information, whilst noisy and diverse is so voluminous that it can provide a key source of information that can support a range of research activities. Transport and traffic accidents are the examples chosen here. At the same time, Melbourne has been named the world's most liveable city, but this city continues to face challenges caused by the continuous population growth and the issues facing the transport systems caused by increased traffic volumes. This has implications for road safety. The Victorian government has implemented a transportation accident black spot programme however this information is largely historic in nature and does not capture real time traffic accidents. We have designed a Cloud-based software system that correlates historic accident black spot information with Twitter and uses this to benchmark the accuracy of Twitter for identifying and verifying the location of traffic accident black spots. The ultimate goal is to use Twitter data to identify accidents in real-time with a measurable degree of confidence.
INDEX TERMS
Accidents, Twitter, Roads, Real-time systems, Media, Cities and towns, Cloud computing
CITATION

R. O. Sinnott and S. Yin, "Accident Black Spot Identification and Verification through Social Media," 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS), Sydney, Australia, 2015, pp. 17-24.
doi:10.1109/DSDIS.2015.34
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