Traffic flow missing data repair method based on improved FCM

CAO Jie; YIN Yangyang; ZHANG Hong; CHEN Zuohan; HOU Liang

doi:10.13873/J.1000-9787(2020)10-0054-04

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Traffic flow missing data repair method based on improved FCM

更新时间：2025-09-11

- Traffic flow missing data repair method based on improved FCM
- Issue 10, Pages: 54-57(2020)
- 作者机构：
  
  1. 兰州理工大学计算机与通信学院
  2. 甘肃省城市轨道交通智能运营工程研究中心
  3. 甘肃省制造业信息化工程研究中心
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)10-0054-04
  CLC： U495;TP311.13
- Published：2020
- 稿件说明：
移动端阅览
CAO Jie, YIN Yangyang, ZHANG Hong, et al. Traffic flow missing data repair method based on improved FCM[J]. 2020, (10): 54-57.
DOI：

CAO Jie, YIN Yangyang, ZHANG Hong, et al. Traffic flow missing data repair method based on improved FCM[J]. 2020, (10): 54-57. DOI： 10.13873/J.1000-9787(2020)10-0054-04.

摘要

针对传统模糊C均值(FCM)算法中聚类数目和模糊度指数由先验知识人为确定而影响聚类结果精度的问题

提出一种改进FCM的交通流缺失数据修复方法。首先根据模糊决策理论

使用FCM目标函数和划分熵共同确定最优模糊度指数;其次利用模拟退火算法优化FCM的聚类数目;最后结合交通流数据的时空相关性提升修复效果。以美国加州运输局运行监测系统提供的高速公路交通数据为研究对象

实验结果表明:该方法克服了传统FCM算法全局搜索能力不足的问题

与传统FCM算法相比

其均方根误差降低了约30%。

Abstract

Aiming at the problem that the number of clustering classes and fuzziness index of the traditional fuzzy C-means（ FCM） algorithm are determined by prior knowledge so affect the precision of clustering results

an improved FCM method for repairing traffic flow missing data is proposed. Firstly

according to the fuzzy decision theory

the FCM objective function and the partition entropy are used together to determine the optimal fuzziness index. Secondly

the simulated annealing algorithm is used to optimize the number of FCM clusters. Finally

the spatial-temporal correlation of traffic flow data is combined to improve the repair effect. The highway traffic data provided by the California Transportation Bureau’s Operation Monitoring System is taken as the research object

and the experimental results show that the method overcomes the problem of insufficient global search ability of the traditional FCM algorithm. Compared with the traditional FCM algorithm

the root mean square error is reduced by about 30 %.

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