|
Abstract
|
We present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets. The traffic detection system was employed for real-time monitoring of several areas of the road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We were also able to discriminate if traffic is caused by an external event or not. Event detection from social networks analysis is a more challenging problem than event detection from traditional media like blogs, emails, etc., where texts are well formatted. SUMs are unstructured and irregular texts, they contain informal or abbreviated words, misspellings or grammatical errors. SUMs contain a huge amount of not useful or meaningless information. In our project, we focus on a particular small-scale event, i.e., road traffic, and we aim to detect and analyze traffic events by processing users' SUMs belonging to a certain area and road traffic. To this aim, we propose a system able to fetch, elaborate, a road traffic event. Tweets are up to 140 characters, enhancing the real-time and news-oriented nature of the platform. In fact, the life-time of tweets is usually very short, thus Twitter is the social network platform that is best suited to study SUMs related to real-time events. It detects the traffic events in real-time; and it is developed as an event-driven infrastructure, built on an SOA architecture.
Keywords: Social media; Traffic detection; Text mining; Privacy; Service Oriented Architecture (SOA), machine learning, Twitter stream analysis
SafetyLit note: We believe that the inclusion of references in this case falls under fair use. Why? 1) This article was published as open access and the journal and copyright owner is acknowledged; 2) a link to full text is provided; 3) reproduction of this reference list is relevant to a commentary on what is and is not original thought and 4) the items in this reference list are part of the data upon which an investigation are based.
References
[1] F. Atefeh and W. Khreich, "A survey of techniques for event detection in Twitter," Comput. Intell., vol. 31, no. 1, pp. 132–164, 2015.
[2] . Ruchi and K. Kamalakar, "ET: Events from tweets," in Proc. 22nd Int. Conf. World Wide Web Comput., Rio de Janeiro, Brazil, 2013, pp. 613–620
[3] A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, "Measurement and analysis of online social networks," in Proc. 7th ACM SIGCOMM Conf. Internet Meas., San Diego, CA, USA, 2007, pp. 29–42.
[4] G. Anastasi et al., "Urban and social sensing for sus- tainable mobility in smart cities," in Proc. IFIP/IEEE Int. Conf. Sustainable Internet ICT Sustainability, Palermo, Italy, 2013, pp. 1–4.
[5] A. Rosi et al., "Social sensors and pervasive services: Approaches and perspectives," in Proc. IEEE Int. Conf. PERCOM Workshops, Seattle, WA, USA, 2011, pp. 525–530.
[6] T. Sakaki, M. Okazaki, and Y. Matsuo, "Tweet analysis for real-time event detection and earthquake reporting system development," IEEE Trans. Knowl. Data Eng., vol. 25, no. 4, pp. 919–931, Apr. 2013.
[7] J. Allan, Topic Detection and Tracking: Event-Based Information Organization. Norwell, MA, USA: Kluwer, 2002.
[8] K. Perera and D. Dias, "An intelligent driver guid- ance tool using location based services," in Proc. IEEE ICSDM, Fuzhou, China, 2011, pp. 246–251.
[9] T. Sakaki, Y. Matsuo, T. Yanagihara, N. P. Chandrasiri, and K. Nawa, "Real-time event extraction for driving information from social sensors," in Proc. IEEE Int. Conf. CYBER, Bangkok, Thailand, 2012, pp. 221–226.
[10] N. Wanichayapong, W. Pruthipunyaskul, Pattara Atikom, and P. Chaovalit, Social based traffic infor- mation extraction and classification in Proc. 11th Int. Conference IT ST, St. Petersburg, Russia, 2011.pp. 107–112.
Keywords: Twitter-Traffic-Status
Language: en |