我院副教授杨翰方在《Journal of Data Science》发表论文。轨迹是一种独特的数据类型，实践中对其隐私性有较高的要求，故针对轨迹的生成模型备受关注。文章对于大规模城市车辆轨迹数据生成问题提出了一种两阶段GAN生成模型，使得轨迹生成过程能够适应大规模数据，又刻画轨迹的宏观和微观特征，并保持多样性。借鉴跨模态方法，在轨迹序列生成中，该模型加入地图路网作为辅助信息，使生成的轨迹和真实路况更为贴近。通过实验，从生成轨迹数据与真实数据在坐标分布、轨迹地理长度、轨迹时间跨度等特征分布上的JS散度，以及生成轨迹与地图路网的匹配精度进行比较，验证了本文提出的方法在生成质量与真实性上普遍优于现有方法。
论文题目
Large Scale GPS Trajectory Generation Using Map based on Two stage GAN
通讯作者介绍
杨翰方，中国人民大学统计学院副教授，博士生导师，应用统计科学研究中心研究员。主要研究方向为经济统计分析、数据科学、机器学习等。在国内外统计学、经济学、机器学习、数据科学等领域期刊或会议中发表多篇论文。主持或参与多项来自国家自然科学基金、科技部、工信部、统计局、政法委等机构的科研项目。
英文摘要
A large volume of trajectory data collected from human beings and vehicle mobility is highly sensitive due to privacy concerns. Therefore, generating synthetic and plausible trajectory data is pivotal in many location-based studies and applications. But existing LSTM-based methods are not suitable for modeling large-scale sequences due to gradient vanishing problem. Also, existing GAN-based methods are coarse-grained. Considering the trajectory’s geographical and sequential features, we propose a map-based Two-Stage GAN method (TSG) to tackle the challenges above and generate fine-grained and plausible large-scale trajectories. In the first stage, we first transfer GPS points data to discrete grid representation as the input for a modified deep convolutional generative adversarial network to learn the general pattern. In the second stage, inside each grid, we design an effective encoder-decoder network as the generator to extract road information from map image and then embed it into two parallel Long Short-Term Memory networks to generate GPS point sequences. Discriminator conditioned on encoded map image restrains generated point sequences in case they deviate from corresponding road networks. Experiments on real-world data are conducted to prove the effectiveness of our model in preserving geographical features and hidden mobility patterns. Moreover, our generated trajectories not only indicate the distribution similarity but also show satisfying road network matching accuracy.
发表页面
期刊介绍
Journal of Data Science创刊于2003年。创刊主编为台湾中研院赵民德博士和台湾辅仁大学谢邦昌博士。本刊历年来发表的研究成果涉及领域广泛，始终致力于推动数据科学方法在各领域的应用。从2019年起，主办权由中国人民大学统计学院和教育部人文社科重点研究基地应用统计科学研究中心承担。为进一步提高学术期刊办刊水平，涵养学术品牌，作为中国人民大学于2019年启动“学术期刊质量提升计划（2019-2021）”重点资助期刊之一，Journal of Data Science酝酿了多方位的改革与提升。