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基于图表示学习的交通拥堵传播模式挖掘与可视分析方法

An Approach to Mine and Visual Analytics the Propagation Patterns of Traffic Congestion based on Graph Representation Learning

  • 摘要: 交通拥堵严重影响居民出行和社会可持续发展, 如何从海量的交通数据中挖掘交通拥堵的传播模式, 制定科学的交通规划方案是一个关键问题. 为此, 提出一种交通拥堵传播模式可视分析方法. 首先基于速度递减指数定义并检测拥堵事件, 通过事件间的时空近邻约束识别拥堵传播; 然后提出集成node2vec模型和VGAE模型的拥堵事件表征算法, 结合降维和聚类算法挖掘拥堵传播模式; 最后设计一个具有多视图联动功能的拥堵传播模式可视分析系统, 支持用户感知拥堵密度, 并交互式地探索拥堵传播模式. 基于某城市连续14天的GPS轨迹数据, 采用平均精度指标进行对比实验, 结果表明, 所提方法在重构拥堵传播上具有更高的准确性. 邀请交通领域和可视化领域的3位专家对所提系统进行评估, 通过2个案例分析, 包括全局拥堵态势感知任务和特定道路拥堵传播模式分析任务, 对系统的易用性、有效性和实用性进行主观评分. 定量评价结果显示, 该系统能够有效协助用户分析拥堵传播模式, 并为制定交通治理方案提供依据.

     

    Abstract: Traffic congestion seriously affects the transportation of residents and the sustainable development of society. How to mine traffic congestion propagation patterns from massive traffic data and formulate a scientific traffic planning scheme is a critical issue. Therefore, a visual analytics method for traffic congestion propagation pattern is proposed. Firstly, congestion events are defined and detected based on the speed reduction index, and spatial and temporal nearest neighbor constraints are used to identify the congestion propagation. Then, an algorithm is proposed to characterize congestion events by integrating node2vec and VGAE models, combined with dimensionality reduction and clustering algorithms, to explore congestion propagation patterns. Finally, a congestion propagation patterns visual analytics system with multi-view linkage is designed to support users to perceive the congestion density and explore the congestion propagation patterns interactively. Based on 14 consecutive days of GPS trajectory data in a city, a comparative experiment using the average accuracy metric is conducted, and the result shows that the proposed method has higher accuracy in reconstructing congestion propagation. Three experts in the fields of transport and visualization are invited to evaluate the proposed system, and subjective scores are given to the ease of use, effectiveness and usefulness of the system through two case studies, including the task of global congestion sensing and the task of analyzing the congestion propagation patterns on specific roads. Quantitative evaluation result shows that the system can effectively assist users in analyzing congestion propagation patterns and provide a basis for formulating traffic management schemes.

     

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