铁路客运需求分析与短期客流预测

Railway Passenger Transport Demand Analysis and Short-term Passenger Flow Forecast

旅客对于铁路客运的需求直接决定了铁路自身的运输生产,对历史旅客需求规律的准确分析以及对未来短期需求的准确预测对旅客运输组织的优化极为重要。为此,提出了基于Prophet模型的铁路客运量需求分析方法和基于Seq2Seq-Attention与Prophet非线性组合模型的短期客流预测方法。前者可以从长期的历史数据中分解出普适整个数据的客流时间分布特征,从而对过去的客运需求规律做出分析;后者利用神经网络进行非线性组合,以求在不同规模的数据集上充分发挥Seq2Seq-Attention网络与Prophet模型各自的优势,做出更精准的客流需求预测。实例验证表明,使用Prophet模型将客流历史数据分解成多种时间分布类型数据后,在整个数据集上的误差仅有6.68%,同时Seq2Seq-Prophet模型在数据集上的预测效果好于组成它的单模型和其余既有方法。

The passenger demand for railway passenger transport directly determines the railway transportation production.It is very important to analyze the historical passenger demand rules accurately and predict the short-term demand in the future.Therefore,a new method of passenger demand analysis based on Prophet model and a short-term passenger flow forecasting method based on Seq2Seq-Attention and Prophet nonlinear combination model was proposed.For the former,the passenger flow time distribution characteristics of the whole data can be decomposed from the long-term historical data,so as to analyze the past passenger transport demand law.For the latter,neural network was used for nonlinear combination in order to give full play to the respective advantages of Seq2Seq attention network and Prophet model on different scale data sets and make more accurate passenger flow demand prediction.The results of the example show that the error of the whole data set is only 6.68%after the passenger flow history data is decomposed into multiple time distribution data by Prophet model.At the same time,the prediction effect of Seq2Seq-Prophet model on the data set is better than that of the single model and other existing methods.