A Deep Learning-based Framework for Intersectional Traffic Simulation and Editing.

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Abstract

Most of existing traffic simulation methods have been focused on simulating vehicles on freeways or city-scale urban networks. However, relatively little research has been done to simulate intersectional traffic to date despite its obvious importance in real-world traffic phenomena. In this paper we propose a novel deep learning-based framework to simulate and edit intersectional traffic. Specifically, based on an in-house collected intersectional traffic dataset, we employ the combination of convolution network (CNN) and recurrent network (RNN) to learn the patterns of vehicle trajectories in intersectional traffic. Besides simulating novel intersectional traffic, our method can be used to edit existing intersectional traffic. Through many experiments as well as comparison user studies, we demonstrate that the results by our method are visually indistinguishable from ground truth and perform better than other methods.