Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize the allocation of rail transit resources. However, the nonlinear and nonstationary nature of passenger flow time series challenges STPFP. To address this issue, a hybrid model based on time series decomposition and reinforcement learning ensemble strategies is proposed. Firstly, the improved arithmetic optimization algorithm is constructed by adding sine chaotic mapping, a new dynamic boundary strategy, and adaptive T distribution mutations for optimizing variational mode decomposition (VMD) parameters. Then, the original passenger flow data containing nonlinear and nonstationary irregular changes of noise is decomposed into several intrinsic mode functions (IMFs) by using the optimized VMD technology, which reduces the time-varying complexity of passenger flow time series and improves predictability. Meanwhile, the IMFs are divided into different frequency series by fluctuation-based dispersion entropy, and diverse models are utilized to predict different frequency series. Finally, to avoid the cumulative error caused by the direct superposition of each IMF’s prediction result, reinforcement learning is adopted to ensemble the multiple models to acquire the multistep passenger flow prediction result. Experiments on four subway station passenger flow datasets proved that the prediction performance of the proposed method was better than all benchmark models. The excellent prediction effect of the proposed model has important guiding significance for evaluating the operation status of urban rail transit systems and improving the level of passenger service.

短期客流预测（STPFP）有助于缓解交通拥堵，优化轨道交通资源配置。然而，客流时间序列的非线性和非平稳性质对STPFP提出了挑战。为了解决这个问题，提出了一种基于时间序列分解和强化学习集成策略的混合模型。首先，通过添加正弦混沌映射、新的动态边界策略和自适应T分布突变来优化变分模式分解（VMD）参数，构建改进的算术优化算法。然后，利用优化的VMD技术，将含有非线性、非平稳、不规则噪声变化的原始客流数据分解为多个本征模态函数（IMF），降低了客流时间序列的时变复杂度，提高了可预测性。同时，通过基于波动的离散熵将IMF分为不同的频率序列，并利用不同的模型来预测不同的频率序列。最后，为了避免各IMF预测结果直接叠加造成的累积误差，采用强化学习对多个模型进行集成，得到多步客流预测结果。在四个地铁站客流数据集上的实验证明，该方法的预测性能优于所有基准模型。该模型优良的预测效果对于评估城市轨道交通系统的运行状况、提高客运服务水平具有重要的指导意义。