基于时间序列的铁路客流量预测及票额优化配置

铁路客流量受多因素影响，其时序特征明显，因此，基于平稳时间序列构建客流数据预测模型及单车次多区间票额分配模型，有利于掌握客流动态变化，改善铁路运营压力。实现特征数据抽取系统开发，进行累加、循环、筛选算法等数据预处理；运用多因子方差分析评价多种因素的显著相关性影响，通过ARMA模型进行短时旅客客流量预测，进行模型优化并检验，同时，基于线性规划构建客座率最大化的区间票额分配优化模型。

Railway passenger flow forecast and ticket allocation optimization based on Time series

Railway passenger flow is affected by many factors, and its timing characteristics are obvious. Therefore, Based on the stationary time series, passenger flow data prediction model and single-ride multi-interval ticket distribution model were constructed., which is beneficial to grasp the dynamic changes of passenger flow and improve the railway operation pressure. The characteristic data extraction system is developed, and carried out data preprocessing such as accumulation, cycle and screening algorithm. Analysis of variance was used to evaluate the significant correlation effect of various factors, the ARMA model was used to predict the short-term passenger flow. The model is optimized and tested. Meanwhile, Based on linear programming, an optimal model of interval ticket allocation for load factor maximization is constructed.