基于类神经网络的轨道交通新开站点客流预测

Passenger Flow Prediction of New Rail Transit Stations Based on Artificial Neural Network

随着轨道交通新线或新开站点的投入使用,其网络拓扑结构会发生变化,并且网络上的客流也变得越发的复杂,如何准确预测新开站点的客流需求是目前轨道交通系统亟需解决的关键问题。因此,充分考虑了新开站点周边用地、拓扑、接驳、居住等特性,构建了基于多因素约束的非线性类神经网络(ANN)模型,对轨道交通新开站点需求进行预测。以北京市轨道交通为例,应用该模型进行预测,其预测精度达到96.6%,说明综合考虑站点用地、拓扑、接驳、居住等因素约束的非线性ANN模型能够较好捕捉新开站点下客流需求的非常规变化,相比以往方法预测精度更高。研究结果可为轨道交通系统新开站点建设立项及可行性研究环节的客流预测提供新的方法。

As the new rail transit lines or newly opened stations are put into use,the network topology has changed,and the passenger flow on the network has become increasingly complex.How to accurately predict the passenger flow demand of the newly opened stations is a key problem that needs to be solved in the current rail transit system.Therefore,the characteristics of land use,topology,connection and residence around the newly opened station were fully considered,and ANN(nonlinear neural network)model based on multi factor constraints was constructed to predict the demand for the newly opened station of rail transit.Then,Beijing rail transit was taked as an example,the prediction accuracy of this model reached 96.6%.The results show that the ANN model,which comprehensively considers constraints of site land use,topology,connection,residence and other factors,can better capture the irregular changes in passenger flow demand under new station opening,and the prediction accuracy is higher than that of the previous methods.The research results can provide new methods for passenger flow prediction in the construction of new stations and feasibility study of rail transit system.