# Incorporating travel behavior regularity into passenger flow forecasting

arXiv: https://arxiv.org/abs/2004.00992 · HTML: https://mlsanity.com/paper/2004.00992/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Probabilistic Traffic Forecasting with Dynamic Regression](https://mlsanity.com/paper/2301.06650/) (2023)
- [Individual Mobility Prediction via Attentive Marked Temporal Point Processes](https://mlsanity.com/paper/2109.02715/) (2021)
- [STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems](https://mlsanity.com/paper/2202.06727/) (2022)
- [Dynamic Graph Representation Learning for Passenger Behavior Prediction](https://mlsanity.com/paper/2408.09092/) (2024)

## Abstract (arXiv)

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.