摘要
提出了一种新的基于时空数据挖掘的铁路客流预测方法,该方法一方面采用统计学原理对目标对象本身的时序进行预测,另一方面通过神经网络解算相邻对象的空间影响,最后使用线性回归得到综合预测结果.采用该方法对某铁路直通区段2004年春运期间旅客总发送量进行预测,与不考虑空间影响的预测方法相比,预测精度有所改善.
By analyzing the limitation of current passenger flow forecast approach, this paper presents a new approach to forecast railway passenger flow based on spatio_temporal data mining. The approach first forecasts time sequence of target object using statistical principles, then figures out the spatial influence of neighbor objects using a neural network, and finally combines the two forecasting results using linear regression. The method is used in the forecast of railway passenger flow during the spring festival period in 2004. Comparing with the existing approaches that don't consider the spatial influence, the forecast accuracy of our approach is better.
出处
《北京交通大学学报》
CAS
CSCD
北大核心
2004年第5期16-19,共4页
JOURNAL OF BEIJING JIAOTONG UNIVERSITY
基金
国家"863"资助项目(2001AA135190)
关键词
时空数据挖掘
客流预测
铁路运输
时空预测
railway transportation
passenger flow forecast
spatio-temporal forecast
spatio-temporal data mining
