Application of the Point-Descriptor-Precedence representation for micro-scale traffic analysis at a non-signalized T-junction

Abstract

ABSTRACTAn intersection of two or more roads poses a risk for potential conflicts among vehicles. Often the reasons triggering such conflicts are not clear, as they might be too subtle for the human eye. The environment also plays a part in understanding where, when, and why a particular vehicle interaction has occurred in a certain way. Therefore, it is of paramount importance to dive deeper into the vehicle interaction at a micro-scale within the embedded geographical environment, particularly at the intersections. This would in turn assist in evaluating the association of vehicle interactions with conflict risks and near-miss accidents. Moreover, detection of such micro traffic interactions could also be used to improvise the complexity of the already established transport infrastructure. Conversely, traffic at intersections has been explored mainly for flow estimation, capacity and width measurements, and traffic congestion, etc., whereas the detection of micro-scale traffic interactions at intersections remains relatively under-explored. In this paper, we present a novel approach to retrieve and represent micro-scale traffic movement interactions at a non-signalized T-junction by extending a recently introduced qualitative spatiotemporal Point-Descriptor-Precedence (PDP) representation. We study how the PDP representation offers a fine solution to study the interaction of traffic flows at intersections. This permits tracking the micro-movement of vehicles in much finer detail, which is used later to retrieve movement patterns from a motion dataset. Unlike conventional approaches, we start our approach with the actual movements before modeling the static intersection environment. Additionally, with the aid of illustrative examples, we discuss how the length, width, and speed of the vehicles can be exploited in our approach to detect specific patterns more accurately. Additionally, we address the potential benefits of our approach for traffic safety assessment and how it can be extended to a network of intersections using different transport modes.

Keywords

• Vehicle interaction
• traffic research
• pattern identification
• traffic safety
• spatiotemporal representation
• movement pattern