Domain-specific data mining for residents' transit pattern retrieval from incomplete information.

The rapid development of Cyber-Physical System (CPS) is gradually cultivating a smart world based on Big Data and computational infrastructures. Such reformation has initiated revolutions in various industries and is gradually reshaping our daily routines. Among these trends, the data-driven intelligentization in Urban Public Transportation Systems can bring the most significant impact to our society, because they provide pervasive reachability. Optimizing and constructing such systems, which requires deep insights into passenger behavioral patterns, is a highly domain-specific data mining task. Although computational infrastructures have provided sufficient processing capacity, the practical utilization of such data is facing several challenges: information alignment in heterogeneous data sources and information enrichment for domain-specific applications. In this article, we first propose a cross-domain method to increase the usability of data by removing the inconsistency in vehicles' positioning and passengers' transaction data. We then use rule-based methods to reconstruct latent mobility information, thereby enabling small grained trajectory based applications. Finally, we provide use cases using the reconstructed information to derive insights to the public transportation system of our target city. Our work can serve as a domain-specific information enrichment and data mining framework for CPS in smart cities.