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A Stacked Generalization Framework for City Traffic Related Geospatial Data Analysis

机译：用于城市交通相关地理空间数据分析的堆叠通用框架

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摘要

Analyzing traffic related geospatial data often lacks in priori knowledge and encounters parameter setting problems due to the dynamic characteristics of city traffic. In this paper, we propose a pervasive, scalable framework for city traffic related geospatial data analysis based on a stacked generalization. Firstly we analyze the optimal linear combination based on stepwise iteration, and also prove its theoretical validity via error-ambiguity decomposition. Secondly we integrate six classical approaches into this framework, including linear least squares regression, autoregressive moving average, historical mean, artificial neural network, radical basis function neural network, support vector machine, and conduct experiments with a real city traffic detecting dataset. We further compare the proposed framework with other four linear combination models. It suggests that the proposed framework behaves more robust than other models both in variance and bias, showing a promising direction for city traffic related geospatial data analysis.
机译：由于城市交通的动态特性，分析与交通有关的地理空间数据通常缺乏先验知识，并且遇到参数设置问题。在本文中，我们提出了一种基于堆栈概括的，可扩展的城市交通相关地理空间数据分析框架。首先，我们分析了基于逐步迭代的最优线性组合，并通过误差-歧义分解证明了其理论有效性。其次，我们将六种经典方法集成到该框架中，包括线性最小二乘回归，自回归移动平均值，历史均值，人工神经网络，根基函数神经网络，支持向量机，并使用真实的城市交通检测数据集进行实验。我们进一步将提出的框架与其他四个线性组合模型进行比较。它表明，所提出的框架在方差和偏差上都比其他模型表现出更强的鲁棒性，显示了城市交通相关地理空间数据分析的有希望的方向。

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  • 来源
    《Web technologies and applications》|2016年|265-276|共12页
  • 会议地点 Suzhou(CN)
  • 作者

    Xiliang Liu; Li Yu; Peng Peng; Feng Lu;

  • 作者单位

    State Key Laboratory of Resources and Environmental Information System, IGSNRR, Chinese Academy of Sciences, Beijing, China,Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou, China;

    State Key Laboratory of Resources and Environmental Information System, IGSNRR, Chinese Academy of Sciences, Beijing, China;

    State Key Laboratory of Resources and Environmental Information System, IGSNRR, Chinese Academy of Sciences, Beijing, China;

    State Key Laboratory of Resources and Environmental Information System, IGSNRR, Chinese Academy of Sciences, Beijing, China,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    City traffic; Geospatial data; Ensemble learning; Stacked generalization; Robustness;

    机译：城市交通；地理空间数据；综合学习；堆叠泛化；坚固性;

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