Toward urban traffic scenarios and more: a spatio-temporal analysis empowered low-rank tensor completion method for data imputation

IF 4.3 1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zilong Zhao, Luliang Tang, Mengyuan Fang, Xue Yang, Chaokui Li, Qingquan Li
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引用次数: 2

Abstract

Abstract Existing traffic monitoring approaches cannot completely cover all road segments in real-time, leading to massive amounts of missing traffic data, which limits the implementation of intelligent transportation systems. Most existing methods lack deep mining of the unique spatiotemporal characteristics of traffic flows, resulting in difficulty in application to urban traffic with complex topologies and variable states. In this paper, we propose a novel Spatio-Temporal constrained Low-Rank Tensor Completion (ST-LRTC) method, which adopts a manifold embedding approach to depict the local geometric structure of spatiotemporal domains. Specifically, under the low-rank assumption, the method introduces temporal constraints based on the continuity and periodicity of traffic flow and a spatial constraint matrix reflecting the traffic flow transmission mechanism. We embed low-dimensional spatiotemporal constraint matrices into the low-rank tensor completion solving process to fully utilize the global features and local spatiotemporal characteristics of the traffic tensor. Experiments were performed using traffic data from Xi’an, China, and the results indicated that ST-LRTC outperformed state-of-the-art methods under various missing rates and patterns. Thorough experiments have demonstrated that the incorporation of spatiotemporal analysis can enhance the adaptability of the tensor completion model to complex urban scenarios, which guarantees better monitoring, diagnosis, and optimization of urban traffic states.
针对城市交通场景和更多:一种时空分析授权的低秩张量补全方法用于数据输入
现有的交通监控方法不能完全实时覆盖所有路段,导致大量交通数据缺失,限制了智能交通系统的实施。现有方法大多缺乏对交通流独特时空特征的深度挖掘,难以应用于拓扑复杂、状态多变的城市交通。本文提出了一种新的时空约束低秩张量补全(ST-LRTC)方法,该方法采用流形嵌入方法来描述时空域的局部几何结构。具体而言,在低秩假设下,该方法引入了基于交通流连续性和周期性的时间约束和反映交通流传输机制的空间约束矩阵。我们将低维时空约束矩阵嵌入到低秩张量补全求解过程中,充分利用交通张量的全局特征和局部时空特征。利用西安的交通数据进行了实验,结果表明ST-LRTC在不同的缺失率和模式下都优于现有的方法。实验表明,结合时空分析可以增强张量补全模型对复杂城市场景的适应性,从而更好地监测、诊断和优化城市交通状态。
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
CiteScore
11.00
自引率
7.00%
发文量
81
审稿时长
9 months
期刊介绍: International Journal of Geographical Information Science provides a forum for the exchange of original ideas, approaches, methods and experiences in the rapidly growing field of geographical information science (GIScience). It is intended to interest those who research fundamental and computational issues of geographic information, as well as issues related to the design, implementation and use of geographical information for monitoring, prediction and decision making. Published research covers innovations in GIScience and novel applications of GIScience in natural resources, social systems and the built environment, as well as relevant developments in computer science, cartography, surveying, geography and engineering in both developed and developing countries.
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