Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Short text classification in twitter to improve information filtering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Streaming first story detection with application to Twitter
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A latent variable model for geographic lexical variation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A Performance evaluation of neural network models in traffic volume forecasting
Mathematical and Computer Modelling: An International Journal
STAR-CITY: semantic traffic analytics and reasoning for CITY
Proceedings of the 19th international conference on Intelligent User Interfaces
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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.