Periodic Residual Learning for Crowd Flow Forecasting

TL;DR

This paper introduces PRNet, a novel periodic residual learning network that models the variation between past and future crowd flow data to improve multi-step forecasting accuracy in smart city applications.

Contribution

PRNet uniquely frames crowd flow forecasting as a residual learning problem focusing on periodic variations, enhancing prediction accuracy over existing methods.

Findings

PRNet improves multi-step crowd flow prediction accuracy.

PRNet can be integrated into existing models easily.

PRNet effectively captures periodic variations in crowd data.

Abstract

Crowd flow forecasting, which aims to predict the crowds entering or leaving certain regions, is a fundamental task in smart cities. One of the key properties of crowd flow data is periodicity: a pattern that occurs at regular time intervals, such as a weekly pattern. To capture such periodicity, existing studies either fuse the periodic hidden states into channels for networks to learn or apply extra periodic strategies to the network architecture. In this paper, we devise a novel periodic residual learning network (PRNet) for a better modeling of periodicity in crowd flow data. Unlike existing methods, PRNet frames the crowd flow forecasting as a periodic residual learning problem by modeling the variation between the inputs (the previous time period) and the outputs (the future time period). Compared to directly predicting crowd flows that are highly dynamic, learning more stationary deviation is much easier, which thus facilitates the model training. Besides, the learned variation enables the network to produce the residual between future conditions and its corresponding weekly observations at each time interval, and therefore contributes to substantially more accurate multi-step ahead predictions. Extensive experiments show that PRNet can be easily integrated into existing models to enhance their predictive performance.