Short-Term Passenger Flow Prediction With Decomposition in Urban Railway Systems

Yangyang Zhao,Xinguo Jiang,Zhenliang Ma,Yi Yang,Wenhua Jiang

doi:10.1109/access.2020.3000242

Abstract

Accurate prediction of short-term passenger flow is vital for real-time operations control and management. Identifying passenger demand patterns and selecting appropriate methods are promising to improve prediction accuracy. This paper proposes a hybrid prediction model with time series decomposition and explores its performance for different types of passenger flows with varied characteristics in urban railway systems. The Seasonal and Trend decomposition using Loess (STL) is used to decompose the passenger flow into the seasonal, trend, and residual time series, representing the constant, long-term fluctuant, and stochastic passenger demand patterns. The approximate entropy (ApEn) is utilized to quantify the predictability of each component. After that, the Holt-Winters (HW) method is developed to predict the seasonal and trend components that both with high predictability. The long short-term memory networks (LSTM NN) are proposed to predict the residual with low predictability. The outputs from these models are combined to predict the short-term passenger flow. We assess the model performance with entry, exit, and transfer passenger flows at the target stations, which are selected via the agglomerative hierarchical clustering (AHC) algorithm in the Shanghai Metro. Compared with three representative models, the results show that 1) the decomposition-based hybrid model performs well for both one-step and multi-step predictions in terms of accuracy and robustness; 2) ApEn is useful for choosing appropriate prediction models.

Highlights

Short-term demand prediction facilitates advanced online applications in public transport, including predictive operations control, service, and customized information provision to passengers [1].Numerous short-term prediction models have been proposed in transportation for different applications, such as travel demand [2], traffic flow [3], travel time [4], and crowding [5]
approximate entropy (ApEn) is utilized to evaluate the predictability of stable, long-term fluctuant, and stochastic passenger demand to lay the basis for choosing suitable for each subsequence
The seasonal component is a smoothing result of the passenger flow time series, the outliers are removed from the raw data

Summary

Introduction

Short-term demand prediction facilitates advanced online applications in public transport, including predictive operations control, service, and customized information provision to passengers [1]. Numerous short-term prediction models have been proposed in transportation for different applications, such as travel demand [2], traffic flow [3], travel time [4], and crowding [5]. Regardless of the varied application scenarios, the short-term prediction in transportation is essentially a time series forecasting problem with exogenous explanatory variables (e.g., weather, land use). In this context, the effectiveness of predictions depends on the match of time series patterns and model assumptions The paper focuses on the station-based short-term passenger flow prediction in urban railway systems

Methods

Results

Conclusion