基于长短期记忆网络的断面交通数据异常处理  

Abnormal detection and correction of section traffic data based on long short-term memory network

作　　者：张灵 康晋滔 成卫[2] ZHANG Ling;KANG Jin-tao;CHENG Wei(Science and Information Office,Traffic Police Branch of Kunming Public Security Bureau,Kunming 650000,China;School of Traffic Engineering,Kunming University of Science and Technology,Kunming 650504,China)

机构地区：[1]昆明市公安局交通警察支队科信处,云南昆明650000 [2]昆明理工大学交通工程学院,云南昆明650504

出　　处：《交通科学与工程》2020年第3期81-87,共7页Journal of Transport Science and Engineering

基　　金：国家自然科学基金(61364019)。

摘　　要：针对基于统计模型的传统算法仅优化局部数据的问题。建立了基于长短期记忆网络深度学习模型的异常数据处理算法和基于Tensorflow框架搭建了神经网络模型。通过进行训练模型,获得备选数据集和校验数据集。通过样本标签对比,判定异常点,并进行数据替换和更新标准样本。为检验该算法的有效性,使用昆汕高速公路K2077断面交通数据,进行验算和分析。研究结果表明:长短期记忆网络模型可快速处理交通时序原始数据,并进行检测修正算法,优化了数据质量,弥补了传统算法的局限性。工作窗口的建立,精简了算法流程,提升了数据修正的精度。In order to solve the contradiction that only the local data can be optimized using traditional algorithms,an abnormal data processing algorithm based on long short-term memory network(LSTM)deep learning model proposed,and then the neural network model is built based on tensor flow framework,so as to obtain alternative data sets and calibration data sets.Through the comparison of sample labels,abnormal points are determined,the data is replaced and the standard samples are updated.To verify the effectiveness of the algorithm,the K2077 section traffic data of Kunming-Shantou expressway was used for checking and analysis.The results show that the LSTM model can quickly process the original traffic time series data and detect and correct algorithm.It can also optimize the data quality and makes up for the limitations of traditional algorithms.The establishment of the working window simplifies the algorithm flow and improves the accuracy of data correction.

关 键 词：智能交通 数据预处理 异常检测与修正 断面交通数据 长短期记忆网络 

分 类 号：U491[交通运输工程—交通运输规划与管理]