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.

智能卡（SC）数据已成为过境乘客行为分析、网络建模和控制优化的主要数据来源之一。在利用智能卡数据调查过境乘客的时空动态或进行其他基于 SC 数据的交通建模之前，起点-终点 (O-D) 估计已被认为是必要的步骤。在最近十年中，现有文献提出了各种基于行程链的方法，用于使用 SC 数据进行交通 OD 估计。然而，传统的旅行链估计方法存在一个问题，但没有得到足够的重视：由于旅行链方法通常每天至少需要两次旅行记录才能进行单笔交易的OD估计。经典行程链方法中的这种缺陷可能会导致大量数据丢失和 OD 估计不准确。本文通过引入基于最小熵率 (MER) 准则的新框架改进了现有的行程链 OD 估计方法。所提出的基于 MER 的方法采用了信息论中类似的降噪机制。我们方法的基本思想是使用替代停靠点来推断单次旅行的下车位置，这些停靠点可以保留乘客在其移动序列中显示的旅行规律。我们的增强方法可以准确地估计单次行程的下车停靠点，从而防止潜在的大量数据丢失。而且，该研究还深入了解了使用旅行序列估计的熵率与乘客旅行规律之间的关系。估计结果可以进一步有益于具有可靠数据源的未来交通研究。