针对大规模轨迹数据集的高效和高质量可视化

近年来，随着城市化的快速发展，城市的规模不断扩大，人们对交通的依赖也 日益增强，出租车、网约车等交通工具已经成为了人们日常出行的重要方式。这 些交通工具普遍配置了定位管理系统，并按照一定的频率采集车辆的位置数据及 时间戳等信息上报至云端数据库中进行储存，为交通管理、路径推荐和城市道路 规划等智慧城市建设任务提供了重要依据。由于城市中轨迹错综复杂，需要对不 同区域内的数据进行比较，可视化就成为了一种在智慧城市分析任务中被广泛应 用的轨迹分析方法，用户通常结合地图对所有的轨迹数据进行可视化来进行洞察 分析和交互式探索。然而如今轨迹数据集往往具有很大的规模（例如深圳单日即 可生成近 30 万条出租车轨迹），对这些大规模的轨迹数据集进行可视化会给城市 分析任务带来诸多问题，例如高延迟交互响应和视觉混乱问题等，严重影响了探 索轨迹数据的过程。 采样是一种直接且能够有效解决大规模数据分析困难问题的方法，被广泛应 用在诸多领域中。对大规模轨迹数据集进行采样可以减少轨迹集的规模，从而降 低可视化的延迟并缓解视觉混乱的问题。但采样通常会丢弃掉部分数据，导致其 中包含的信息量减少，同时采样后结果和采样前的全集数据不同会导致采样后轨 迹的可视化视图与采样前的可视化视图存在巨大差异，降低了可视化的视觉质量， 对后续的探索分析造成误导。经过对国内外轨迹采样方法的调研发现，现有的方 法都无法同时满足（1）生成与采样前可视化视图相似的高质量轨迹可视化和（2） 低延迟交互分析这两个智慧城市分析任务中的核心需求。 为了解决上述问题，本文提出了一种具有理论保障的轨迹可视化探索框架，对 大规模轨迹数据进行高效采样和可视化，在降低数据量的同时尽量减少最终可视 化视觉信息损失。本文的贡献可以概括为：（1）本文首先定义了用来度量两个轨迹 可视化视图之间相似性的视觉质量函数，并在此基础上提出了质量最优采样问题 （𝑄𝑢𝑎𝑙𝑖𝑡𝑦 𝑂𝑝𝑡𝑖𝑚𝑎𝑙 𝑆𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑃𝑟𝑜𝑏𝑙𝑒𝑚，𝑄𝑂𝑆𝑃）。（2）本文设计了具有视觉质量保 证的采样算法 𝑉𝑖𝑠𝑢𝑎𝑙 𝑄𝑢𝑎𝑙𝑖𝑡𝑦 𝐺𝑢𝑎𝑟𝑎𝑛𝑡𝑒𝑒𝑑 𝑆𝑎𝑚𝑝𝑙𝑖𝑛𝑔（𝑉𝑄𝐺𝑆）作为 𝑄𝑂𝑆𝑃 的解 决方案，并引入了轨迹数据分布和人类感知两大关键特性，提出了 𝑉𝑄𝐺𝑆+ 优化算 法，在保证视觉质量的同时解决了视觉混乱的问题。（3）为了提供低延迟的交互分 析保障，本文进一步设计了一个在线交互式轨迹可视化框架 𝐶ℎ𝑒𝑒𝑡𝑎ℎ𝑇𝑟𝑎𝑗，融合了 一套原创性空间索引 𝐼𝑛𝑣𝑄𝑢𝑎𝑑 来提升计算效率，并允许使用离线计算完成的轨迹采样结果集进行在线分析。（4）本文在三个真实世界的轨迹数据集对 𝐶ℎ𝑒𝑒𝑡𝑎ℎ𝑇𝑟𝑎𝑗 做了全面的验证，并搜集了最前沿且应用最广泛的三个轨迹采样算法进行对比分 析，设计了包括案例分析、用户调研和性能分析三种不同的分析验证方案。实验结 果表明，本文提出的 𝐶ℎ𝑒𝑒𝑡𝑎ℎ𝑇𝑟𝑎𝑗 框架始终可以提供比现有的其他轨迹采样和可 视化方法更高的视觉质量和计算效率，与可视化所有轨迹相比，𝐶ℎ𝑒𝑒𝑡𝑎ℎ𝑇𝑟𝑎𝑗 在 避免视觉混乱的同时可以将可视化延迟降低多达 3 个数量级。

In recent years, with the rapid development of urbanization, the scale of cities has been continuously expanding, and people's reliance on public transportation has increasingly strengthened. Among these, public transportation modes such as taxis and ridehailing services have become an important means of daily travel for people. These public transport vehicles are generally equipped with positioning system to collect real-time vehicle latitude and longitude data and timestamp information at a certain frequency and upload it to the cloud database for storage, and form continuous trajectory data based on timestamp information. The trajectory data of these taxis and ride-hailing vehicles is important data sources for traffic management, route recommendation, and urban road planning tasks in the construction of smart cities. At the same time, analysis of trajectory data can also help enterprises such as ride-hailing companies better analyze urban road conditions to plan driving routes more optimally. Visualization is a method widely used in smart city analysis tasks, where users usually combine maps to visualize all trajectory data for insight analysis and exploration. However, trajectory datasets often have a large scale, for example, there will be nearly 300,000 taxi trajectories generated in Shenzhen every day, and visualizing these large-scale trajectory datasets can bring many problems to urban analysis tasks, such as high-latency interaction response and visual clutter issues, severely affecting the analysis process. Sampling is a direct and effective method to solve the difficulties of large-scale data analysis, widely applied in many fields. Sampling large-scale trajectory datasets can reduce the size of the trajectory set, thereby reducing visualization latency and alleviating visual clutter issues. However, sampling usually discards some data, which may lead to the results after sampling being different from the full dataset before sampling, thereby causing the visualization results of the sampled trajectories to be significantly different from those before sampling, misleading subsequent exploration and analysis. Existing sampling methods for trajectories cannot simultaneously meet the two core needs of smart city analysis: (1) high-quality trajectory visualization similar to the pre-sampling visualization view and (2) low-latency interaction analysis after sampling. To solve this problem, this thesis proposes a trajectory visualization exploration framework with theoretical guarantees, achieving efficient sampling of large-scale trajectory datasets, and ensuring high-quality visualization and low-latency interaction of trajectory analysis. This thesis first defines a visual quality function based on similarity to measure the visual quality between two trajectory visualization views, and on this basis, proposes the 𝑄𝑢𝑎𝑙𝑖𝑡𝑦 𝑂𝑝𝑡𝑖𝑚𝑎𝑙 𝑆𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑃𝑟𝑜𝑏𝑙𝑒𝑚(𝑄𝑂𝑆𝑃). To solve 𝑄𝑂𝑆𝑃, this thesis designs a sampling algorithm with visual quality guarantees, 𝑉𝑖𝑠𝑢𝑎𝑙 𝑄𝑢𝑎𝑙𝑖𝑡𝑦 𝐺𝑢𝑎𝑟𝑎𝑛𝑡𝑒𝑒𝑑 𝑆𝑎𝑚𝑝𝑙𝑖𝑛𝑔 (𝑉𝑄𝐺𝑆), and introduces two key features of trajectory data distribution and human perception to propose the VQGS+ optimization algorithm, solving the visual clutter issue while ensuring visual quality. To provide lowlatency interactive analysis, this thesis further designs and develops an online interactive trajectory visualization framework, 𝐶ℎ𝑒𝑒𝑡𝑎ℎ𝑇𝑟𝑎𝑗, integrating an original quadtree-based index, 𝐼𝑛𝑣𝑄𝑢𝑎𝑑, to enhance computational efficiency, and allowing for online analysis with offline-computed trajectory sampling result sets. To validate the effectiveness of the framework proposed in this thesis, extensive experiments were conducted on three real-world trajectory datasets, collecting the most cutting-edge and widely used three trajectory sampling algorithms for comparative analysis, and designed including case studies, user surveys, and quantitative research three different analysis and verification plans. The experimental results show that the 𝐶ℎ𝑒𝑒𝑡𝑎ℎ𝑇𝑟𝑎𝑗 framework proposed in this thesis consistently provides higher visual quality and computational efficiency than existing trajectory sampling and visualization methods. And compared to visualizing all trajectories, 𝐶ℎ𝑒𝑒𝑡𝑎ℎ𝑇𝑟𝑎𝑗 can reduce visualization latency by up to three orders of magnitude while avoiding visual clutter.

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