主题：Dynamic Auto-Structuring Graph Neural Network: A Joint Learning Framework for Origin-Destination Demand Prediction

　　主讲人：肖峰

　　讲座时间：2022年11月6日10:00-11:30

　　形式：腾讯会议（ID：408-554-609）

　　主办单位：管理工程学院

　　讲座摘要：Solving the demand prediction problem is an important part of improving the efficiency and reliability of ride-hailing services. Spatial-temporal graph learning methods have shown potential in modelling the spatial-temporal dependencies of ride-hailing demand data, but most existing studies focus on region-level demand prediction with only a few researchers addressing the problem of origin-destination (OD) demand prediction. In addition, previous spatial-temporal graph learning methods employ pre-defined and rigid graph structures that do not reveal the instinct and dynamic dependencies of ride-hailing demand data. In this paper, we propose a joint learning framework called Dynamic Auto-structuring Graph Neural Network (DAGNN) to address the origin-destination demand prediction problem. We develop a Dynamic Graph Decomposition and Recombination layer (DGDR) to handle both the graph structure and the graph representation learning problems simultaneously, with graph representations learned from a group of trainable and time-aware edge-induced subgraphs. Experimental results show that our proposed model outperforms ten baseline models with two real-world ride-hailing demand datasets and is efficient in structural pattern discovery. Comparing with existing methods, the significant advantage of the proposed method is that it circumvents the difficulties in defining the underlying graph structure of the researched data.

　　主讲人简介：肖峰，工学博士，教授，博士生导师。现任西南财经大学人工智能与管理科学研究中心主任、大数据研究院副院长。国家杰出青年基金获得者。研究方向主要包括人工智能算法与数据挖掘、复杂交通系统建模优化、金融风控与智能投顾、区块链等。在管理科学与工程、交通科技及数据挖掘领域国际期刊和会议如Transportation Science，Transportation Research Part A、B、C、D, IEEE TKDE、ISTTT等发表论文40余篇。研究团队与香港科技大学，香港理工大学，美国加州大学伯克利分校、戴维斯分校，加拿大多伦多大学，英国利兹大学，清华大学等国内外著名高校保持着密切合作和访学交流关系。