摘要
为提升客流量预测精度,提出基于深度学习的轨道交通客流量预测模型。首先,通过自助站台系统收集乘客的进出站信息。其次,初步处理数据,包括数据清洗、归一化整理。最后,整合不同模型中的客流量数据,以揭示它们之间的相关性。基于深度学习中的卷积神经网络算法,构建了一种新型的轨道交通客流量预测模型,该模型利用历史客流数据进行训练,并能够自动学习数据中的复杂特征和规律,从而精准预测未来客流量变化。实验结果显示,所设计的模型精度达到89.91%,表明新模型在客流量预测准确性方面取得了显著的提升。
In order to improve the accuracy of passenger flow prediction,a rail passenger flow prediction model based on deep learning is proposed.Firstly,collect passenger entry and exit information through the self-service platform system.Secondly,preliminary data processing includes data cleaning and normalization.Finally,integrate passenger flow data from different models to reveal their correlation.Based on the convolutional neural network algorithm in deep learning,a new prediction model of rail transit passenger flow is constructed,which uses the historical passenger flow data for training,and can automatically learn the complex characteristics and rules in the data,so as to realize the accurate prediction of the future passenger flow changes.The experimental results show that the accuracy of the designed model has reached 89.91%,indicating that the new model has achieved a significant improvement in the accuracy of passenger flow prediction.
作者
刘帆
朱强
LIU Fan;ZHU Qiang(School of Management,Zhengzhou University of Economics and Business,Zhengzhou Henan 451191,China;School of Computer Science,Zhengzhou University of Economics and Business,Zhengzhou Henan 451191,China)
出处
《信息与电脑》
2023年第22期63-65,共3页
Information & Computer
基金
河南省软科学研究计划项目“基于深度学习的郑州地铁进出站客流量分析及预警研究”(项目编号:222400410485)。
关键词
轨道交通
客流量预测
深度学习算法
rail transit
passenger flow forecast
deep learning algorithms
