Highway Traffic Modeling Using Probabilistic Petri Net Models

Abstract

In this paper, we propose a novel method for modeling the highway traffic using Probabilistic Petri nets (PPNs). More specifically, the highway has been partitioned into discrete segments and probabilistic measures are derived based on the traffic data considering the vehicle movements. The proposed model is validated through the study of a publicly available dataset called Active Transportation Demand Management (ATDM) Trajectory Level Validation, which provides the real traffic data from different driving scenarios. The proposed method will generate graphical structures of the PPN as well as all important attributes related to the real traffic data. The output model can be used for path planning and collision avoidance for highway traffic.