Minimum entropy rate-improved trip-chain method for origin–destination estimation using smart card data

Abstract

Smart card (SC) data has become one of the major data sources for transit passengers’ behavior analysis, network modeling, and control optimization. Origin–destination (O–D) estimation has been recognized as a requisite step before utilizing the smart card data to investigate transit passengers’ spatiotemporal dynamics or conduct other SC data-based transit modeling. In the recent decade, the extant literature has proposed various trip-chain-based methods for transit O-D estimation using SC data. However, one problem of the conventional trip-chaining estimation approach has been noticed but not paid enough attention to: O-D estimation of single transactions cannot be conducted since the trip-chain method generally requires at least two trip records per day to proceed with. Such a flaw in the classic trip-chain approach might lead to a considerable amount of data loss and inaccurate O-D estimation. This paper improved the existing trip-chain O-D estimation method by introducing a new framework based on the Minimum Entropy Rate (MER) criterion. The proposed MER-based method adopts a similar mechanism of noise reduction in information theory. The basic idea of our approach is to infer the alighting location of single trips using alternative stops that preserve passengers’ travel regularity exhibiting in their mobility sequences. Our enhanced approach can estimate alighting stops for single trips with decent accuracy, thus preventing a potential massive data loss. Moreover, the study also provides an in-depth insight into the relationship between entropy rates estimated using trip sequences and passengers’ travel regularity. The estimation results can further benefit future transit studies with reliable data sources.