高速铁路客站商业网点租价评估神经网络模型应用研究

A Study on Neural Network Applied in Evaluation System of Commercial Premises Rent Pricing in High-Speed Railway Station

对于高速铁路运营方而言,在客运站科学合理地设置一定体量的商业、广告设施,既有利于提升服务品质,又能提升整体经营效益。基于BP、RBF等神经网络模型组合,构建高速铁路客运车站商业网点租价评估指标,通过逐步回归、交叉检验等数据处理和算法优化手段,建立高速铁路客站商业网点租价评估神经网络模型。通过评估模型仿真预测结果与实际经营结果的比较分析,模型预测精度较高,可以用于日常经营分析应用,为高速铁路客站商业网点智能化提供应用依据。

For the high-speed railway operator, setting a certain amount of commercial and advertising facilities scientifically and reasonably in station are not only to improve service, but also to drive the overall economic efficiency. Based on the combination of BP and RBF neural network models, by using the theory of retailing science, we build an evaluation system of commercial premises rent pricing in high speed railway station. Then through data processing and algorithm optimization, such as stepwise regression and cross-examination, an application plan of rent price evaluation system is proposed in this paper. The comparison and analysis of the evaluation model simulation prediction results and the practical operation results show that the model prediction is accurate, and it can be used in daily operation, which provides reference for building intelligent commercial management system in the future.