Traffic forecasting models rely on data that needs to be sensed, processed, and stored. This requires the deployment and maintenance of traffic sensing infrastructure, often leading to unaffordable monetary costs. The lack of sensed locations can be complemented with synthetic data simulations that further lower the economical investment needed for traffic monitoring. One of the most common data generative approaches consists of producing real-like traffic patterns, according to data distributions from analogous roads. The process of detecting roads with similar traffic is the key point of these systems. However, without collecting data at the target location no flow metrics can be employed for this similarity-based search. We present a method to discover locations among those with available traffic data by inspecting topological features of road segments. Relevant topological features are extracted as numerical representations (embeddings) to compare different locations and eventually find the most similar roads based on the similarity between their embeddings. The performance of this novel selection system is examined and compared to simpler traffic estimation approaches. After finding a similar source of data, a generative method is used to synthesize traffic profiles. Depending on the resemblance of the traffic behavior at the sensed road, the generation method can be fed with data from one road only. Several generation approaches are analyzed in terms of the precision of the synthesized samples. Above all, this work intends to stimulate further research efforts towards enhancing the quality of synthetic traffic samples and thereby, reducing the need for sensing infrastructure.

该研究提出一种通过检查拓扑特征来发现具有相似交通流量特征的交通路段的方法，该方法可以通过数据的合成来尽可能减少对交通监测所需的经济投入。研究还分析了几种基于此方法的数据生成方法，并与简单的流量估计方法进行了比较。

一种基于图形的无感知估计道路交通特征的方法