ShanghaiTech University Knowledge Management System
| Early Forecast of Traffic Accident Impact Based on a Single-Snapshot Observation (Student Abstract) | |
| 2022-06-30 | |
| 会议录名称 | PROCEEDINGS OF THE 36TH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI 2022 |
| 卷号 | 36 |
| 页码 | 13015-13016 |
| 发表状态 | 已发表 |
| 摘要 | Predicting and quantifying the impact of traffic accidents is necessary and critical to Intelligent Transport Systems (ITS). As a state-of-the-art technique in graph learning, current graph neural networks heavily rely on graph Fourier transform, assuming homophily among the neighborhood. However, the homophily assumption makes it challenging to characterize abrupt signals such as traffic accidents. Our paper proposes an abrupt graph wavelet network (AGWN) to model traffic accidents and predict their time durations using only one single snapshot. Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
| 会议录编者/会议主办者 | Association for the Advancement of Artificial Intelligence |
| 关键词 | Accidents Graph neural networks Intelligent systems Intelligent vehicle highway systems 'current Early forecasts Graph Fourier transforms Graph neural networks Homophily Intelligent transport Neighbourhood Single snapshots State-of-the-art techniques Transport systems |
| 会议名称 | 36th AAAI Conference on Artificial Intelligence, AAAI 2022 |
| 会议地点 | Virtual, Online |
| 会议日期 | February 22, 2022 - March 1, 2022 |
| 收录类别 | EI |
| 语种 | 英语 |
| 出版者 | Association for the Advancement of Artificial Intelligence |
| EI入藏号 | 20230713571492 |
| EI主题词 | Forecasting |
| EI分类号 | 406.1 Highway Systems ; 723.4 Artificial Intelligence ; 723.5 Computer Applications ; 914.1 Accidents and Accident Prevention |
| 原始文献类型 | Conference article (CA) |
| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/282040 |
| 专题 | 信息科学与技术学院_硕士生 |
| 作者单位 | 1.University of Notre Dame, United States; 2.Shanghai Tech University, China; 3.South Dakota State University, United States; 4.University of Nevada, Las Vegas, United States; 5.Virginia Tech, United States; 6.Mississippi State University, United States |
| 推荐引用方式 GB/T 7714 | Meng, Guangyu,Jiang, Qisheng,Fu, Kaiqun,et al. Early Forecast of Traffic Accident Impact Based on a Single-Snapshot Observation (Student Abstract)[C]//Association for the Advancement of Artificial Intelligence:Association for the Advancement of Artificial Intelligence,2022:13015-13016. |
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