基于高峰期热门目的地识别的旅客买短乘长行为预估方法
Estimating passenger's act of buying short distance ticket for long-distance travel based on identifying popular destinations during peak period
孔德越 1程默 1袁磊磊 1周姗琪 1王洪业1
作者信息
- 1. 中国铁道科学研究院集团有限公司 电子计算技术研究所,北京 100081
- 折叠
摘要
旅客集中出行的节假日高峰期间,旅客买短乘长行为成为困扰客运组织管理的难题.文章提出一种基于高峰期热门目的地识别的旅客买短乘长行为预估模型,通过分析历史客流规律与城市出行热度,实现热门车次短途旅客买短乘长风险概率评估.选取2023年高峰期列车实际运营及补票数据对该模型进行检验,结果显示,其整体均方误差为0.26％,表明该模型具备实际应用条件;试用情况表明,该模型可为客运管理部门保障高峰期列车安全运营提供有效决策依据.
Abstract
During peak holiday periods when passengers are concentrated,the act of buying short distance ticket for long-distance travel has become a difficult problem for passenger transport organization and management.This paper proposed a prediction model for passenger's act of buying short distance ticket for long-distance travel based on identifying popular destinations during peak period.The paper analyzed historical passenger flow patterns and urban travel popularity,implemented the risk probability assessment of short distance passengers buying short distance ticket for long-distance travel on popular train numbers,selected actual train operation and ticket replenishment data during peak hours in 2023 to test the model.The results show that the overall mean square error was 0.26％.It indicates that the model has practical application conditions.The trial results show that this model can provide effective decision-making basis for passenger transport management departments to ensure the safe operation of trains during peak hours.
关键词
出行热度/买短乘长/客票/热门目的地/超员停车Key words
travel popularity/buying short distance ticket for long-distance travel/ticket/popular tourist destinations/stopped due to overload引用本文复制引用
基金项目
中国铁道科学研究院集团有限公司科研项目(2023YJ135)
出版年
2024
NETL
NSTL
万方数据