Discovering traffic congestion through traffic flow patterns generated by moving object trajectories

摘要

The discovery of moving object trajectory patterns representing high traffic density has been covered in various works using diverse approaches. These models are useful in areas such as transportation planning, traffic monitoring, and advertising on public roads. However, though studies tend to recognize the importance of these types of patterns in utility, they usually do not consider traffic congestion as a particular condition of high traffic. In this work, we present a model for the discovery of high traffic flow patterns in relation to traffic congestion. This relationship is represented in terms of traffic that is shared between different sectors of the pattern, making it possible to identify traffic flow situations causing congestion. We also complement this model by discovering alternative paths for the severe traffic depicted in these patterns. These alternative paths depend on traffic level and location inside the road network. Depending on the traffic conditions, alternative paths are commonly sought by drivers when they are approaching a traffic jam, in order to mitigate the effects of traffic congestion. We compare these models with related work from similar areas and validate them by conducting experiments using real data. We describe discovered patterns related to the main elements of the road network in the dataset and show their advantages in comparison to related models. Based on the displayed metrics, the algorithms' implementation offers good performance execution for the given dataset volume. The results presented confirm the usefulness of the proposed patterns as a tool that helps to improve traffic, allowing the identification of problems and possible alternatives.