Improving traffic prediction with tweet semantics

Jingrui He, Wei Shen, Phani Divakaruni, Laura Wynter, Rick Lawrence

Research output: Chapter in Book/Report/Conference proceedingConference contribution

61 Scopus citations

Abstract

Road traffic prediction is a critical component in modern smart transportation systems. It provides the basis for traffic management agencies to generate proactive traffic operation strategies for alleviating congestion. Existing work on near-term traffic prediction (forecasting horizons in the range of 5 minutes to 1 hour) relies on the past and current traffic conditions. However, once the forecasting horizon is beyond 1 hour, i.e., in longer-term traffic prediction, these techniques do not work well since additional factors other than the past and current traffic conditions start to play important roles. To address this problem, in this paper, for the first time, we examine whether it is possible to use the rich information in online social media to improve longer-term traffic prediction. To this end, we first analyze the correlation between traffic volume and tweet counts with various granularities. Then we propose an optimization framework to extract traffic indicators based on tweet semantics using a transformation matrix, and incorporate them into traffic prediction via linear regression. Experimental results using traffic and Twitter data originated from the San Francisco Bay area of California demonstrate the effectiveness of our proposed framework.

Original languageEnglish (US)
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Pages1387-1393
Number of pages7
StatePublished - Dec 1 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: Aug 3 2013Aug 9 2013

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Other

Other23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
CountryChina
CityBeijing
Period8/3/138/9/13

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    He, J., Shen, W., Divakaruni, P., Wynter, L., & Lawrence, R. (2013). Improving traffic prediction with tweet semantics. In IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence (pp. 1387-1393). (IJCAI International Joint Conference on Artificial Intelligence).