NDLI: Identification of (near) Real-time Traffic Congestion in the Cities of Australia through Twitter

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Identification of (near) Real-time Traffic Congestion in the Cities of Australia through Twitter

Content Provider	ACM Digital Library
Author	Deng, Fengmin Sinnott, Richard O. Gong, Yikai
Abstract	Transport congestion is an increasing problem especially for larger cities. Typically traffic conditions are monitored in Australia by state and/or federal authorities using expensive electronic devices/sensors on roads or through CCTV cameras. However there is an alternative and far cheaper way to monitor real-time traffic status on roads: through targeted social media analytics. Social networking sites such as Twitter are hugely popular, public and often real-time in nature. A growing number of people post tweets about their lives and feelings every day and everywhere, often with location-based service information included. In this paper, we present an architecture and novel harvesting and analytics approach that exploits this information to identify near real-time transport congestion. Specifically, we present an algorithm for targeted harvesting of tweets solely on the road network using the definitive road network data for Australia. We then implement spatial-temporal clustering algorithms to identify spatio-temporal clusters of tweets on roads to identify potential traffic congestion. We show the scalability of the solution through the use of the large-scale Cloud facilities offered through the National eResearch Collaboration Tools and Resources (NeCTAR -- www.nectar.org.au) Research Cloud.
Starting Page	7
Ending Page	12
Page Count	6
File Format	PDF
ISBN	9781450337861
DOI	10.1145/2811271.2811276
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Gis Social media Data mining Transport
Content Type	Text
Resource Type	Article

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