关键词: 交通工程；固定检测器；动态交通数据；故障数据识别；数据修复；优化算法

Abstract: In order to realize effective identification and repair of fault data, a fault data identification algorithm based on outlier distance detection and an improved DE-LSTM data repair model were proposed to address the problem that fixed detectors were prone to abnormal and missing traffic data during the collection of dynamic traffic data. By utilizing the inherent continuity of time series data, effective identification of fault data was achieved through direct outlier localization and outlier distance detection. The differential evolution algorithm was used to optimize the number of hidden layer neurons and the initial learning rate of long short-term memory neural network, and the adaptive control strategy was introduced to improve the mutation factor and crossover factor in the traditional DE algorithm. The repair model of long short-term memory neural network based on the improved differential evolution algorithm was established and compared with the fixed threshold combined with traffic flow mechanism, LSTM neural network model and DE-LSTM repair model. The example verification results indicate that compared with the fixed threshold combined with traffic flow mechanism method, the outlier distance detection algorithm has a more efficient recognition rate, and the improved DE-LSTM model has good computational efficiency and repair performance.

Key words: traffic engineering; fixed detector; dynamic traffic data; fault data identification; data repair; optimization algorithm