基于非负矩阵分解的OD矩阵预测
发布时间：2018-07-14 11:46
【摘要】：提出了一种非负矩阵分解-自回归模型,并用该模型对居民出行流量进行预测.该模型首先利用非负矩阵分解方法挖掘城市区域内的居民出行特征,而后在非负矩阵分解获得的特征矩阵和系数矩阵基础上对时序系数矩阵建立自回归模型,进而对起讫矩阵进行预测.以北京市出租车数据为基础,与时空权重K近邻、传统K近邻、反向神经网络、朴素贝叶斯、随机森林和C4.5决策树回归模型对比,实验结果表明,该模型的预测准确率有显著提升.
[Abstract]:A non negative matrix decomposition autoregressive model is proposed, and the model is used to predict the travel traffic. The model first uses the nonnegative matrix decomposition method to excavate the residents' travel characteristics in the urban area, and then builds the autoregressive model of the time series coefficient matrix on the basis of the characteristic matrix and the coefficient matrix obtained by the nonnegative matrix decomposition. On the basis of Beijing taxi data, it is compared with the time and space weight K nearest neighbor, the traditional K nearest neighbor, the reverse neural network, the naive Bayesian, the random forest and the C4.5 decision tree regression model. The experimental results show that the prediction accuracy of the model has been significantly improved.
【作者单位】： 西南大学计算机与信息科学学院;
【基金】：国家自然科学基金项目(61403315,61402379)
【分类号】：U491.12
，
本文编号：2121547
[Abstract]:A non negative matrix decomposition autoregressive model is proposed, and the model is used to predict the travel traffic. The model first uses the nonnegative matrix decomposition method to excavate the residents' travel characteristics in the urban area, and then builds the autoregressive model of the time series coefficient matrix on the basis of the characteristic matrix and the coefficient matrix obtained by the nonnegative matrix decomposition. On the basis of Beijing taxi data, it is compared with the time and space weight K nearest neighbor, the traditional K nearest neighbor, the reverse neural network, the naive Bayesian, the random forest and the C4.5 decision tree regression model. The experimental results show that the prediction accuracy of the model has been significantly improved.
【作者单位】： 西南大学计算机与信息科学学院;
【基金】：国家自然科学基金项目(61403315,61402379)
【分类号】：U491.12
，
本文编号：2121547
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