CATOM : Causal Topology Map for Spatiotemporal Traffic Analysis with Granger Causality in Urban Areas.

Chanyoung Jung, Soobin Yim, Giwoong Park, Simon Oh, Yun Jang
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Abstract

The transportation network is an important element in an urban system that supports daily activities, enabling people to travel from one place to another. One of the key challenges is the network complexity, which is composed of many node pairs distributed over the area. This spatial characteristic results in the high dimensional network problem in understanding the 'cause' of problems such as traffic congestion. Recent studies have proposed visual analytics systems aimed at understanding these underlying causes. Despite these efforts, the analysis of such causes is limited to identified patterns. However, given the intricate distribution of roads and their mutual influence, new patterns continuously emerge across all roads within urban transportation. At this stage, a well-defined visual analytics system can be a good solution for transportation practitioners. In this paper, we propose a system, CATOM (Causal Topology Map), for the cause-effect analysis of traffic patterns based on Granger causality for extracting causal topology maps. CATOM discovers causal relationships between roads through the Granger causality test and quantifies these relationships through the causal density. During the design process, the system was developed to fully utilize spatial information with visualization techniques to overcome the previous problems in the literature. We also evaluate the usability of our approach by conducting a SUS(System Usability Scale) test and traffic cause analysis with the real-world data from two study sites in collaboration with domain experts.

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CATOM:用于城市地区格兰杰因果关系时空交通分析的因果拓扑图。
交通网络是城市系统的重要组成部分,它支持人们的日常活动,使人们能够从一个地方前往另一个地方。主要挑战之一是网络的复杂性,它由分布在整个区域的许多节点对组成。这一空间特征导致了在理解交通拥堵等问题的 "成因 "方面存在高维网络问题。最近的研究提出了旨在了解这些根本原因的可视化分析系统。尽管做出了这些努力,但对这些原因的分析仅限于已识别的模式。然而,由于道路分布错综复杂且相互影响,城市交通中的所有道路都会不断出现新的模式。在这个阶段,一个定义明确的可视化分析系统可以为交通从业人员提供一个很好的解决方案。本文提出了一种基于格兰杰因果关系的交通模式因果分析系统 CATOM(因果拓扑图),用于提取因果拓扑图。CATOM 通过格兰杰因果检验发现道路之间的因果关系,并通过因果密度量化这些关系。在设计过程中,系统充分利用了空间信息和可视化技术,克服了以往文献中存在的问题。我们还与领域专家合作,通过对两个研究地点的真实数据进行 SUS(系统可用性量表)测试和交通原因分析,评估了我们方法的可用性。
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From Dashboard Zoo to Census: A Case Study With Tableau Public. Authoring Data-Driven Chart Animations. Super-NeRF: View-consistent Detail Generation for NeRF Super-resolution. Iceberg Sensemaking: A Process Model for Critical Data Analysis. CATOM : Causal Topology Map for Spatiotemporal Traffic Analysis with Granger Causality in Urban Areas.
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