Accurate forecasting of passenger flow (i.e., ridership) is critical to the operation of urban metro systems. Previous studies mainly model passenger flow as time series by aggregating individual trips and then perform forecasting based on the values in the past several steps. However, this approach essentially overlooks the fact that passenger flow consists of trips from each individual traveler. For example, a traveler’s work trip in the morning can help predict his/her home trip in the evening, while this causal structure cannot be explicitly encoded in standard time series models. In this paper, we propose a new forecasting framework for boarding flow by incorporating the generative mechanism into standard time series models and leveraging the strong regularity rooted in travel behavior. In doing so, we introduce returning flow from previous alighting trips as a new covariate, which captures the causal structure and long-range dependencies in passenger flow data based on travel behavior. We develop the return probability parallelogram (RPP) to summarize the causal relationships and estimate the return flow. The proposed framework is evaluated using real-world passenger flow data, and the results confirm that the returning flow—a single covariate—can substantially and consistently improve various forecasting tasks, including one-step ahead forecasting, multi-step ahead forecasting, and forecasting under special events. And the proposed method is more effective for business-type stations with most passengers come and return within the same day. This study can be extended to other modes of transport, and it also sheds new light on general demand time series forecasting problems, in which causal structure and long-range dependencies are generated by the user behavior.

准确预测客流（即载客量）对于城市地铁系统的运行至关重要。先前的研究主要通过汇总各个行程来将旅客流量建模为时间序列，然后根据过去几个步骤中的值进行预测。但是，这种方法基本上忽略了这样一个事实，即客流由来自每个单独旅行者的旅行组成。例如，旅行者在早上的工作旅行可以帮助预测他/她在晚上的回家旅行，而这种因果关系无法在标准的时间序列模型中明确编码。在本文中，我们通过将生成机制纳入标准时间序列模型并利用植根于旅行行为的强规律性，提出了一个新的登机流预测框架。在这样做，我们将以前的下车旅程的回程流量作为一个新的协变量引入，该变量捕获基于旅行行为的客流数据中的因果结构和长期依存关系。我们开发了返回概率平行四边形（RPP）以总结因果关系并估计返回流量。使用现实世界的客流数据对提议的框架进行了评估，结果证实，回程（单个协变量）可以显着，持续地改善各种预测任务，包括提前一步预测，提前多步预测和预测在特殊事件下。所提出的方法对于在同一天内来回最多乘客的商务型车站更有效。这项研究可以扩展到其他运输方式，