[论文解读] Trajectory Flow Map: Graph-based Approach to Analysing Temporal Evolution of Aggregated Traffic Flows in Large-scale Urban Networks

Jiwon Kim, Kai Zheng+3 more|arXiv (Cornell University)|Dec 6, 2022

Human Mobility and Location-Based Analysis被引用 4

一句话总结

本文提出轨迹流图（Trajectory Flow Map），一种基于图的新型方法，可将大规模城市轨迹数据转化为随时间演化的图结构，以分析城市范围内的交通动态。通过将城市空间划分为若干空间单元，并将单元间的流量建模为带权有向边，该方法能够借助图挖掘技术检测出行驶模式中的结构性突变点（如高峰时段到非高峰时段的转换），从而提供一种紧凑且可解释的时间交通演化表征。

ABSTRACT

This paper proposes a graph-based approach to representing spatio-temporal trajectory data that allows an effective visualization and characterization of city-wide traffic dynamics. With the advance of sensor, mobile, and Internet of Things (IoT) technologies, vehicle and passenger trajectories are being increasingly collected on a massive scale and are becoming a critical source of insight into traffic pattern and traveller behaviour. To leverage such trajectory data to better understand traffic dynamics in a large-scale urban network, this study develops a trajectory-based network traffic analysis method that converts individual trajectory data into a sequence of graphs that evolve over time (known as dynamic graphs or time-evolving graphs) and analyses network-wide traffic patterns in terms of a compact and informative graph-representation of aggregated traffic flows. First, we partition the entire network into a set of cells based on the spatial distribution of data points in individual trajectories, where the cells represent spatial regions between which aggregated traffic flows can be measured. Next, dynamic flows of moving objects are represented as a time-evolving graph, where regions are graph vertices and flows between them are treated as weighted directed edges. Given a fixed set of vertices, edges can be inserted or removed at every time step depending on the presence of traffic flows between two regions at a given time window. Once a dynamic graph is built, we apply graph mining algorithms to detect change-points in time, which represent time points where the graph exhibits significant changes in its overall structure and, thus, correspond to change-points in city-wide mobility pattern throughout the day (e.g., global transition points between peak and off-peak periods).

研究动机与目标

• 解决大规模城市轨迹数据的分析挑战，以理解城市范围内的交通动态。
• 开发一种可扩展且可解释的聚合交通流在城市网络中的表征方法。
• 检测随时间变化的移动模式中的显著结构性变化，例如高峰时段与非高峰时段之间的转换。
• 通过动态图结构实现城市交通时间演化的可视化与表征。

提出的方法

• 基于轨迹数据点的空间分布，将城市网络划分为若干空间单元。
• 每个时间窗口生成一个动态图，其中顶点代表空间单元，有向加权边代表单元之间的聚合交通流。
• 在每个时间步，根据单元对之间是否存在流量，动态地插入或删除边。
• 由此生成的时间演化图结构能够捕捉交通模式的时空演化特征。
• 应用图挖掘技术检测突变点——即图整体结构发生显著变化的时间点。
• 这些突变点对应于城市整体移动行为的重大转变，如通勤高峰的转换。

实验结果

研究问题

• RQ1 如何将大规模城市轨迹数据有效转化为能够捕捉交通流时间演化的动态图表征？
• RQ2 交通流图中的哪些结构性变化对应于城市移动模式的有意义转变？
• RQ3 基于图的方法能否检测出城市交通动态中的显著突变点，例如高峰到非高峰时段的转换？
• RQ4 所提出的轨迹流图如何实现复杂城市移动模式的紧凑且可解释的可视化？

主要发现

• 轨迹流图成功将大规模城市交通表示为一系列随时间演化的图结构，从而实现对时空流量动态的可视化。
• 通过图挖掘进行的突变点检测能够识别出行驶行为中的关键时间转换，例如通勤高峰的开始或午间低峰期的出现。
• 该方法提供了一种紧凑且信息丰富的图抽象表征，有效降低了数据复杂度，同时保留了关键的结构模式。
• 该方法在仅使用轨迹数据和空间划分的前提下，展示了检测城市移动模式全局转变的可行性。