Public transit is described by a wide range of data, which include sensor data, open data, and social network data. Data come in large real-time streams, and are heterogeneous. How to integrate such data in real time? We propose MOBility ANAlyzer (MOBANA), a distributed stream-based framework. MOBANA deals with the integration of heterogeneous information, processing efficiency, and redundancy reduction. As far as integration is concerned, MOBANA integrates data at different layers, and converts them into exchangeable data formats. Specifically, to integrate feed information, MOBANA uses an improved incremental text classifier, based on Kullback Leibler distance. As far as efficiency is concerned, MOBANA is implemented by distributed stream processing engine and distributed messaging system, which enable scalable, efficient, and reliable real-time processing. Specifically, within the transport domain, MOBANA identifies the real-time position of vehicles by an as-needed adjustment of planned position against the real-time position, thus dropping network load. As far as redundancy is concerned, MOBANA filters tweets through a three-fold similarity analysis, which encompasses geo-location, text, and image. In addition, MOBANA is a complete framework, which has been tested as a pilot with real data in the city of Pavia, Italy.
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