Reconstructing individual mobility from smart card transactions: a collaborative space alignment approach

Smart card transactions capture rich information of human mobility and urban dynamics and therefore are of particular interest to urban planners and location-based service providers. However, since most transaction systems are only designated for billing purpose, typically, fine-grained location information, such as the exact boarding and alighting stops of a bus trip, is only partially or not available at all, which blocks deep exploitation of this rich and valuable data at individual level. This paper presents a collaborative space alignment framework to reconstruct individual mobility history from a metropolitan-scale smart card transaction dataset. Specifically, we show that by delicately aligning the monetary space and geospatial space with the temporal space, we are able to extrapolate a series of critical domain-specific constraints. Later, these constraints are naturally incorporated into a semi-supervised conditional random field (CRF) to infer the exact boarding and alighting stops of all transit routes, where the features of the CRF model consist of not only pre-defined indicator features extracted from individual trips but also latent features crafted from different users' trips using collaborative filtering. Here, we consider two types of collaborative features: (1) the similarity in terms of users' choices of bus lines and (2) latent temporal patterns of users' commuting behaviors. Extensive experimental results show that our approach achieves a high accuracy, e.g., given only 10 % trips with known alighting/boarding stops, and we successfully inferred more than 79 % alighting and boarding stops from all unlabeled trips. In particular, we validated that the extracted collaborative features significantly contribute to the accuracy of our model. In addition, we have demonstrated that by applying our approach to enrich the data, the performance of a conventional method for identifying users' home and work places can be dramatically improved (with 83 % improvement on home detection and 38 % improvement on work place detection). The proposed method offers the possibility to mine individual mobility from common public transit transactions, and showcases how uncertain data can be leveraged with domain knowledge and constraints, to support cross-application data-mining tasks.