广西师范大学学报（自然科学版）2024，Vol.42Issue(2)：30-40,11.DOI:10.16088/j.issn.1001-6600.2023051402
基于动态生成对抗网络的路网缺失交通数据修复
Missing Traffic Data Recovery for Road Network Based on Dynamic Generative Adversarial Network
摘要
Abstract
For the issue of missing data occurring in the data collection process of intelligent transportation systems,a road network traffic data recovery model is proposed in this paper based on the dynamic generative adversarial network.Firstly,the approach in this paper are constructed various missing traffic data matrices by considering the spatial-temporal properties of traffic data and the set missing patterns and missing rates.Then iteratively trains a GAN was composed of two fully connected neural networks based on a game idea.By introducing a novel dynamic adaptive mechanism,this study can automatically identify the optimal number of iterations of the generator and discriminator during the model computation,and finally generates the complete traffic data matrix and repair the missing values.California PeMS and Guangzhou traffic speed datasets are used to complete the D-GAN model construction,and multiple evaluation metrics are employed to assess the repair performance of D-GAN.Experimental results show that the repair accuracy of D-GAN is higher for random missing patterns compared with non-random missing patterns;and the repair accuracy of D-GAN degrades accelerated with increasing missing rates.However,the repair performance of D-GAN outperforms the baseline models(e.g.,BGCP,prophet-RF,and GAIN)under various missing conditions.关键词
智能交通系统/交通数据修复/生成对抗网络/博弈思想/动态自适应机制Key words
intelligent transportation systems/traffic data recovery/generative adversarial network/game idea/dynamic adaptive mechanism分类
交通工程引用本文复制引用
许伦辉,李金龙,李若南,陈俊宇..基于动态生成对抗网络的路网缺失交通数据修复[J].广西师范大学学报（自然科学版）,2024,42(2):30-40,11.基金项目
国家自然科学基金(52072130) （52072130）
广东省普通高校重点领域专项(2021ZDZX1077) （2021ZDZX1077）
广东省重点建设学科科研能力提升项目(2021ZDJS116) （2021ZDJS116）
