TrafPS:基于 Shapley 的可视化分析方法来解读交通流量

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Zezheng Feng, Yifan Jiang, Hongjun Wang, Zipei Fan, Yuxin Ma, Shuang-Hua Yang, Huamin Qu, Xuan Song
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引用次数: 0

摘要

深度学习(DL)的最新成果证明了其在预测交通流量方面的潜力。这种预测有利于了解情况并做出交通控制决策。然而,大多数最先进的深度学习模型都被视为 "黑箱",其底层机制对终端用户来说几乎不透明。以前的一些研究试图 "打开黑箱",提高生成预测的可解释性。然而,在大规模时空数据上处理复杂的模型,以及发现对交通流有重大影响的显著时空模式,仍然具有挑战性。为了克服这些挑战,我们提出了 TrafPS,这是一种用于解释交通预测结果的可视化分析方法,可为交通管理和城市规划决策提供支持。我们提出了区域 SHAP 和轨迹 SHAP 测量方法,以量化不同层次的交通流模式对城市交通的影响。根据领域专家的任务要求,我们采用了交互式可视化界面,对重要的流动模式进行多角度探索和分析。两个实际案例研究证明了 TrafPS 在识别关键路线和为城市规划提供决策支持方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

TrafPS: A shapley-based visual analytics approach to interpret traffic

TrafPS: A shapley-based visual analytics approach to interpret traffic

Recent achievements in deep learning (DL) have demonstrated its potential in predicting traffic flows. Such predictions are beneficial for understanding the situation and making traffic control decisions. However, most state-of-the-art DL models are considered “black boxes” with little to no transparency of the underlying mechanisms for end users. Some previous studies attempted to “open the black box” and increase the interpretability of generated predictions. However, handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain challenging. To overcome these challenges, we present TrafPS, a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning. The measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different levels. Based on the task requirements from domain experts, we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow patterns. Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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