Toward a social sensor based framework for intelligent transportation

Abstract

Intelligent Transportation Systems (ITSs) are supposed to constantly provide drivers with useful navigation information (e.g., the shortest driving paths, real-time traffic conditions, and other road condition data) by collecting raw data from physical sensors and various media. Previous work mainly considers how to develop ITS frameworks and how to extract useful information from physical sensors. Meanwhile, the relatively rich user-contributed postings which contain valuable information about traffic and road conditions was not leveraged to enhance the effectiveness of ITSs. To address such a research gap, this paper presents a relatively new approach of extracting and analyzing useful driving navigation information from the big data archived on online social media, the so-called social sensors. In particular, we advocate a topic model-based computational method to extract relevant semantics (e.g., traffic conditions, road conditions, drivers' conditions, etc.) from the relatively noisy user-contributed postings on online social media. Then, a classification ensemble method is proposed to automatically identify specific traffic related events, and transmit these useful navigation hints back to an ITS for dissemination to other drivers. Evaluation based on real-world user postings from Twitter and Sina Weibo demonstrates that the proposed social sensor analytics method is effective in identifying main traffic events when compared to other baseline methods. Our work opens the door for the development of the next generation of ITSs which can leverage the big data archived on online social media to enhance the richness and the quality of traffic navigation aids provided by ITSs.