School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel). 2020 Apr 2;20(7):1992. doi: 10.3390/s20071992.
Various traffic-sensing technologies have been employed to facilitate traffic control. Due to certain factors, e.g., malfunctioning devices and artificial mistakes, missing values typically occur in the Intelligent Transportation System (ITS) sensing datasets, resulting in a decrease in the data quality. In this study, an integrated imputation algorithm based on fuzzy C-means (FCM) and the genetic algorithm (GA) is proposed to improve the accuracy of the estimated values. The GA is applied to optimize the parameter of the membership degree and the number of cluster centroids in the FCM model. An experimental test of the taxi global positioning system (GPS) data in Manhattan, New York City, is employed to demonstrate the effectiveness of the integrated imputation approach. Three evaluation criteria, the root mean squared error (RMSE), correlation coefficient (R), and relative accuracy (RA), are used to verify the experimental results. Under the ±5% and ±10% thresholds, the average RAs obtained by the integrated imputation method are 0.576 and 0.785, which remain the highest among different methods, indicating that the integrated imputation method outperforms the history imputation method and the conventional FCM method. On the other hand, the clustering imputation performance with the Euclidean distance is better than that with the Manhattan distance. Thus, our proposed integrated imputation method can be employed to estimate the missing values in the daily traffic management.
已经采用了各种交通感应技术来实现交通管制。由于某些因素，例如设备故障和人为错误，智能交通系统（ITS）感应数据集中通常会出现缺失值，从而降低数据质量。在本研究中，提出了一种基于模糊 C 均值（FCM）和遗传算法（GA）的集成插补算法，以提高估计值的准确性。GA 用于优化 FCM 模型中隶属度和聚类中心数的参数。通过对纽约市曼哈顿出租车全球定位系统（GPS）数据的实验测试，验证了集成插补方法的有效性。采用均方根误差（RMSE）、相关系数（R）和相对精度（RA）三个评估标准来验证实验结果。在±5%和±10%的阈值下，集成插补方法获得的平均 RA 分别为 0.576 和 0.785，在不同方法中保持最高，表明集成插补方法优于历史插补方法和传统 FCM 方法。另一方面，基于欧几里得距离的聚类插补性能优于基于曼哈顿距离的聚类插补性能。因此，我们提出的集成插补方法可用于估计日常交通管理中的缺失值。
