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A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation

机译：一种基于模糊C均值的插补方法与遗传算法相结合的混合方法

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摘要

Although various innovative traffic sensing technologies have been widely employed, incomplete sensor data is one of the most major problems to significantly degrade traffic data quality and integrity. In this study, a hybrid approach integrating the Fuzzy C-Means (FCM)-based imputation method with the Genetic Algorithm (GA) is develop for missing traffic volume data estimation based on inductance loop detector outputs. By utilizing the weekly similarity among data, the conventional vector-based data structure is firstly transformed into the matrix-based data pattern. Then, the GA is applied to optimize the membership functions and centroids in the FCM model. The experimental tests are conducted to verify the effectiveness of the proposed approach. The traffic volume data collected at different temporal scales were used as the testing dataset, and three different indicators, including root mean square error, correlation coefficient, and relative accuracy, are utilized to quantify the imputation performance compared with some conventional methods (Historical method, Double Exponential Smoothing, and Autoregressive Integrated Moving Average model). The results show the proposed approach outperforms the conventional methods under prevailing traffic conditions.
机译：尽管各种创新的交通传感技术已被广泛采用，但不完整的传感器数据仍是严重降低交通数据质量和完整性的最主要问题之一。在这项研究中，开发了一种基于模糊C均值（FCM）的插补方法与遗传算法（GA）集成的混合方法，用于基于电感环路检测器输出的丢失交通量数据估计。通过利用数据之间的每周相似性，传统的基于矢量的数据结构首先被转换为基于矩阵的数据模式。然后，将遗传算法应用于FCM模型中的隶属函数和质心优化。进行实验测试以验证所提出方法的有效性。与传统方法相比（Historical method，Historical method，Historical method，Historical method，Historical method，Historical method，Historical双指数平滑和自回归综合移动平均模型）。结果表明，在当前交通状况下，该方法优于传统方法。

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  • 来源
    《Transportation research》 |2015年第2期|29-40|共12页
  • 作者

    Jinjun Tang; Guohui Zhang; Yinhai Wang; Hua Wang; Fang Liu;

  • 作者单位

    School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;

    Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA;

    School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China,Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA;

    School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;

    School of Energy and Traffic Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Missing sensor data; Fuzzy C-means; Genetic algorithm; Imputation; Traffic volume;

    机译：缺少传感器数据;模糊C均值;遗传算法归因;流量;

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