大数据下针对高铁增值服务优化的算法研究

Algorithm Research on Optimization of High-speed rail Value-added Services under Big Data

通过分析高铁旅客增值服务系统目前存在的产品价格高、服务针对性差的问题,从高铁旅客增值服务需求出发,借助铁路客运大数据平台构建指标体系,提出了利用深度学习算法,依据旅客基本属性预测旅客所需增值服务的思路。并运用旅客出发到达地、旅客年龄、籍贯以及所乘坐席等要素对算法可靠性进行了检验。

This article analyzes the current high-speed rail passenger value-added service system's current problems of high product prices and poor service pertinence.Starting from the high-speed rail passenger value-added service demand,using the railway passenger transport big data platform to build an indicator system,it proposes the use of deep learning algorithms based on passengers’basic attributes predict the idea of value-added services required by passengers.The reliability of the algorithm is tested using factors such as the place of departure and arrival of the passenger,the age of the passenger,the place of origin,and the seat taken.