Overcoming Catastrophic Forgetting in Continual Fine-Grained Urban Flow Inference

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Xovee Xu, Ting Zhong, Haoyang Yu, Fan Zhou, Goce Trajcevski
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引用次数: 0

Abstract

Citywide fine-grained urban flow inference (FUFI) problem aims to infer the high-resolution flow maps from the coarse-grained ones, which plays an important role in sustainable and economic urban computing and intelligent traffic management. Previous models tackle this problem from spatial constraint, external factors and memory cost. However, utilizing the new urban flow maps to calibrate the learned model is very challenging due to the “catastrophic forgetting” problem and is still under-explored. In this paper, we make the first step in FUFI and present CUFAR – Continual Urban Flow inference with augmented Adaptive knowledge Replay – a novel framework for inferring the fine-grained citywide traffic flows. Specifically, (1) we design a spatial-temporal inference network that can extract better flow map features from both local and global levels; (2) then we present an augmented adaptive knowledge replay (AKR) training algorithm to selectively replay the learned knowledge to facilitate the learning process of the model on new knowledge without forgetting. We apply several data augmentation techniques to improve the generalization capability of the learning model, gaining additional performance improvements. We also propose a knowledge discriminator to avoid the “negative replaying” issue introduced by noisy urban flow maps. Extensive experiments on two large-scale real-world FUFI datasets demonstrate that our proposed model consistently outperforms strong baselines and effectively mitigates the forgetting problem.
克服连续细粒度城市流推断中的灾难性遗忘
城市细粒度流量推断(FUFI)问题旨在从粗粒度流量图推断出高分辨率流量图,这在可持续、经济的城市计算和智能交通管理中发挥着重要作用。以往的模型从空间限制、外部因素和内存成本等方面来解决这一问题。然而,由于 "灾难性遗忘 "问题的存在,利用新的城市流图来校准所学模型是非常具有挑战性的,目前还没有得到充分的研究。在本文中,我们迈出了 FUFI 的第一步,并提出了 CUFAR - 利用增强型自适应知识重放进行连续城市流量推断 - 一种用于推断细粒度全市交通流量的新型框架。具体来说,(1) 我们设计了一个时空推理网络,可以从局部和全局两个层面提取更好的流量图特征;(2) 然后,我们提出了一种增强型自适应知识重放(AKR)训练算法,选择性地重放已学知识,以促进模型对新知识的学习过程,而不会遗忘。我们应用了几种数据增强技术来提高学习模型的泛化能力,从而获得额外的性能改进。我们还提出了一种知识判别器,以避免由噪声城市流图带来的 "负重放 "问题。在两个大规模真实 FUFI 数据集上进行的广泛实验表明,我们提出的模型始终优于强大的基线模型,并有效缓解了遗忘问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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