Research on Passenger Flow Prediction of Urban Rail Transit Based on Grey Improvement Model

Abstract

In recent years, China's urban rail transit sector has undergone rapid expansion, with passenger demand consistently increasing. Accurate passenger flow forecasting is essential for ensuring efficient and safe metro operations. This paper takes Nantong Metro Line 1 as a case study and applies an optimized forecasting approach that integrates a grey metabolism model with the Holt double-parameter exponential smoothing method. Based on an analysis of Automated Fare Collection (AFC) data from March 2023 to February 2024, passenger flow on this line demonstrates a clear linear growth trend, which aligns well with the assumptions of the grey metabolism model. The results indicate that the optimized grey metabolism model not only significantly enhances prediction accuracy but also greatly reduces the variance ratio, demonstrating high reliability in forecasting outcomes. This improved methodology provides a more robust tool for metro operators in planning services, managing capacity, and optimizing resource allocation.