Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2013

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.

Discipline

Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

IJCAI '13: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, Beijing, China, August 3-9

First Page

1387

Last Page

1393

ISBN

9781577356332

Identifier

10.5555/2540128.2540328

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.5555/2540128.2540328

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