Incorporating travel behavior regularity into passenger flow forecasting
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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 with strong regularity rooted in their travel behavior. For example, a traveler's work trip in the morning can help predict his/her home trip in the evening, while this fact cannot be explicitly encoded in standard time series models. In this paper, we propose a new passenger flow forecasting framework by incorporating the generative mechanism into standard time series models. In doing so, we focus on forecasting boarding demand, and 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 benefit various forecasting tasks, including one-step ahead forecasting, multi-step ahead forecasting, and forecasting under special events. 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 the long-range dependencies are generated by the behavior patterns of users.
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