城市轨道交通客流量预测的统计分析
Statistical Analysis of Passenger Flow Forecasting for Urban Rail Transit
摘要: 研究依据2015年到2023年期间重庆市轨道交通的月度客流量数据，运用自回归积分滑动平均模型(ARIMA)、长短期记忆网络(LSTM)，以及ARIMA与LSTM相结合的混合模型，对2024年的客流量加以预测。
Abstract: This study utilizes monthly passenger flow data from Chongqing’s rail transit system between 2015 and 2023 to forecast passenger volume in 2024. The analysis employs three approaches: An Autoregressive Integrated Moving Average (ARIMA) model, a Long-Term Short-Term Memory (LSTM) network, and a hybrid model combining both frameworks.
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