Spatiotemporal Nonrecurring Traffic Spillback Pattern Prediction for Freeway Merging Bottleneck Using Conditional Generative Adversarial Nets with Simulation Accelerated Training

Forecasting short-term, nonrecurring traffic dynamics caused by incidents is an essential capability in the Intelligent Transportation Systems. This research proposes a prediction framework in which Conditional Deep Convolutional Generative Adversarial Nets (C-DCGAN) is trained to predict the traffic spillbacks patterns associated with freeway incidents at merging bottleneck. Speed tensors, which depict the spatiotemporal incident-induced impacts for multiple neighboring routes, is a suitable object for the GAN model to understand and predict. Further, we demonstrated how to use the mesoscopic Dynamic Traffic Assignment (DTA) model DynusT to generate a large number of training data, thus speeding up the model training. The developed model achieves both statistical and spatial similarities between predicted speed tensors and actual tensors, to 83.84%. To the best of our knowledge, this line of work is one of the first attempts in the literature to train the Machine Learning model to predict speed tensor representation of multi-location incident-induced spatiotemporal impact at merging bottleneck and speeding up the training via simulation.