S-MGHSTN: Towards An Effective Streaming Traffic Accident Risk Prediction Framework

IF 10.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Minxiao Chen;Haitao Yuan;Nan Jiang;Zhihan Zheng;Zhifeng Bao;Ao Zhou;Jiaxin Jiang;Shangguang Wang
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引用次数: 0

Abstract

Traffic accidents pose a significant risk to human health and property safety. To address this issue, predicting their risks has garnered growing interest. We argue that a desired prediction solution should demonstrate resilience to the complexity of traffic accidents. In particular, it should adequately consider the streaming nature of data and key related aspects, such as regional background, accurately capture both proximity and similarity while bridging the disparities, and effectively address the sparsity. However, these factors are often overlooked or difficult to incorporate. In this paper, we propose a novel streaming multi-granularity hierarchical spatio-temporal network. Initially, we innovate by incorporating remote sensing data, facilitating the creation of hierarchical multi-granularity structure and the comprehension of regional background. We construct multiple high-level risk prediction tasks to enhance model’s ability to cope with sparsity. Subsequently, to capture and bridge spatial proximity and semantic similarity, region features and multi-view graph undergo encoding processes to distill effective representations, followed by a graph-enhanced representation alignment module that reconciles their disparities. At last, an alternating experience replay with a dual-memory buffer is employed to accommodate streaming data scenarios. Extensive experiments on two real datasets verify the superiority of our model against the state-of-the-art methods.
S-MGHSTN:一种有效的流交通事故风险预测框架
交通事故对人类健康和财产安全构成重大威胁。为了解决这个问题,预测它们的风险引起了越来越多的兴趣。我们认为,一个理想的预测解决方案应该表现出对交通事故复杂性的弹性。特别是,应充分考虑数据的流性质和关键相关方面,如区域背景,在弥合差异的同时准确捕捉接近性和相似性,并有效解决稀疏性问题。然而,这些因素往往被忽视或难以纳入。在本文中,我们提出了一种新颖的流多粒度分层时空网络。首先,我们通过结合遥感数据进行创新,促进了分层多粒度结构的创建和对区域背景的理解。我们构建了多个高层次的风险预测任务,以增强模型应对稀疏性的能力。随后,为了捕获和桥接空间接近性和语义相似性,区域特征和多视图图进行编码过程以提取有效的表示,然后使用图形增强的表示对齐模块来协调它们的差异。最后,采用双内存缓冲的交替体验重放来适应流数据场景。在两个真实数据集上进行的大量实验验证了我们的模型相对于最先进方法的优越性。
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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