计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 505-511.doi: 10.11896/JsJkx.190700045
张德干, 范洪瑞, 龚倡乐, 高瑾馨, 张婷, 赵彭真, 陈晨
ZHANG De-gan, FAN Hong-rui, GONG Chang-le, GAO Jin-xin, ZHANG Ting, ZHAO Peng-zhen and CHEN Chen
摘要: 面对当前庞大的智慧交通数据量,收集并统计处理是必要且重要的过程,但无法避免的数据缺失问题是目前的研究重点。文中针对车辆交通数据缺失问题提出一种基于张量的车辆交通数据缺失估计新方法:集成贝叶斯张量分解(Integrated Bayesian Tensor Decomposition,IBTD)。该算法在数据模型构建阶段,利用随机采样原理,将缺失数据随机抽取生成数据子集,并用优化后的贝叶斯张量分解算法进行插补。引入集成思想,将多个插补后的误差结果进行分析排序,考虑时空复杂度,择优平均得到最优结果。通过平均绝对百分比误差之后(Mean Absolute Percentage Error,MAPE)和均方根误差(Root Mean Square Error,RMSE)对提出模型的性能进行评估。实验结果表明,所提新方法能够有效地对不同缺失量的交通数据集进行插补,并能得到很好的插补结果。
中图分类号:
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