Inferring trip destination in smart card data with only tap-in control is an important application. Most existing methods estimate trip destinations based on the continuity of trip chains, while the destinations of isolated/unlinked trips cannot be properly handled. We address this problem with a probabilistic topic model. A three-dimensional latent dirichlet allocation model is developed to extract latent topics of departure time, origin, and destination among the population; each passenger’s travel behavior is characterized by a latent topic distribution defined on a three-dimensional simplex. Given the origin station and departure time, the most likely destination can be obtained by statistical inference. Furthermore, we propose to represent stations by their rank of visiting frequency, which transforms divergent spatial patterns into similar behavioral regularities. The proposed destination estimation framework is tested on Guangzhou Metro smart card data, in which the ground-truth is available. Compared with benchmark models, the topic model not only shows increased accuracy but also captures essential latent patterns in passengers’ travel behavior. The proposed topic model can be used to infer the destination of unlinked trips, analyze travel patterns, and passenger clustering.

中文翻译：

---

基于智能卡数据的目的地推断和出行模式挖掘的概率模型

仅通过接入控制在智能卡数据中推断行程目的地是一项重要应用。大多数现有方法基于旅行链的连续性来估计旅行目的地，而无法正确处理孤立/未链接旅行的目的地。我们用概率主题模型来解决这个问题。建立三维潜在狄利克雷分配模型，提取人群中的出发时间、出发地、目的地等潜在主题；每个乘客的旅行行为的特征在于定义在 3 维单纯形上的潜在主题分布。给定始发站和出发时间，可以通过统计推断得到最可能的目的地。此外，我们建议通过访问频率的排名来表示站点，它将不同的空间模式转化为相似的行为规律。所提出的目的地估计框架在广州地铁智能卡数据上进行了测试，其中地面实况是可用的。与基准模型相比，主题模型不仅显示出更高的准确性，而且还捕获了乘客旅行行为中的基本潜在模式。提出的主题模型可用于推断未链接旅行的目的地、分析旅行模式和乘客聚类。