Defining Traffic States using Spatio-temporal Traffic Graphs
This work addresses traffic congestion analysis at intersections for developing countries with mixed and lane-less traffic, but it is incremental as it applies existing graph-based methods to a new dataset.
The authors tackled the problem of distinguishing congested from normal traffic behavior at intersections in developing countries by using spatio-temporal traffic graphs to identify states like clumping, unclumping, and neutral, and they introduced the EyeonTraffic dataset with 3 hours of aerial videos from 3 intersections in Ahmedabad, India, showing that their method helps correctly identify congestion-prone behavior.
Intersections are one of the main sources of congestion and hence, it is important to understand traffic behavior at intersections. Particularly, in developing countries with high vehicle density, mixed traffic type, and lane-less driving behavior, it is difficult to distinguish between congested and normal traffic behavior. In this work, we propose a way to understand the traffic state of smaller spatial regions at intersections using traffic graphs. The way these traffic graphs evolve over time reveals different traffic states - a) a congestion is forming (clumping), the congestion is dispersing (unclumping), or c) the traffic is flowing normally (neutral). We train a spatio-temporal deep network to identify these changes. Also, we introduce a large dataset called EyeonTraffic (EoT) containing 3 hours of aerial videos collected at 3 busy intersections in Ahmedabad, India. Our experiments on the EoT dataset show that the traffic graphs can help in correctly identifying congestion-prone behavior in different spatial regions of an intersection.