铁道运输与经济2024，Vol.46Issue(4)：27-33,67,8.DOI:10.16668/j.cnki.issn.1003-1421.2024.04.04
基于实时流处理的自适应冰雪爱好者旅客群体划分模型研究
Research on an Adaptive Passenger Group Division Model for Ice and Snow Enthusiasts Based on Real-time Stream Processing
摘要
Abstract
Accurate and efficient identification of passenger groups is one of the key factors in formulating operation strategies and improving passenger service quality.Based on the real-name ticket purchase data and travel data of railway passengers,the individual characteristics and social relationship characteristics of passengers were constructed and vectorized.Taking into overall consideration the complexity and heavy calculation workload of the existing deep learning model with better accuracy,this paper,combining knowledge distillation and edge calculation,achieved model compression based on knowledge distillation to reduce memory cost and accelerate calculation.At the same time,segmenting the training model and adopting the nearest real-time computing characteristics of edge computing,it deployed part of the split model in the application server node,thus reducing the reasoning delay and energy consumption of the model,so as to adapt to the real-time division of the passenger group in real-time ticketing.Finally,with ice and snow express service recommendation as an example,it verified the accuracy and prediction efficiency of the model,and provided decision-making suggestions on personalized passenger transport product design and optimized marketing strategy of the railway for passenger groups.关键词
产品设计/个体特征/社交关系/群体划分/边缘计算/知识蒸馏Key words
Product Design/Individual Characteristics/Social Relationship/Group Division/Edge Calculation/Knowledge Distillation分类
交通工程引用本文复制引用
梅巧玲,郝晓培,杨立鹏,易超..基于实时流处理的自适应冰雪爱好者旅客群体划分模型研究[J].铁道运输与经济,2024,46(4):27-33,67,8.基金项目
国家重点研发计划项目(2020YFF0304101) （2020YFF0304101）
