Using Conditional Generative Adversarial Nets and Heat Maps with Simulation-Accelerated Training to Predict the Spatiotemporal Impacts of Highway Incidents

Abstract

<jats:p> An increasingly emphasized research area is the forecast of short-term traffic conditions for nonrecurring traffic dynamics caused by random highway incidents such as crashes or roadway closures. This research proposes a prediction framework which focuses on training a machine learning (ML) model to predict the speed heatmap associated with incidents. Heatmaps contain ideal information that depicts the spatiotemporal characteristics of incident-induced impacts and are suitable objects for ML models to understand and predict. Because of the sparsity of incident data in the real world, we proposed a simulation approach to rapidly expand the training dataset, thus speeding up the model training process. The conditional deep convolutional generative adversarial nets is employed to predict the speed heatmap and the mesoscopic dynamic traffic assignment model DynusT was used to generate many training data. The evaluation shows that the proposed model captures both the tonal and spatial distribution of pixel values at 80.19% similarity between the prediction and actual heatmaps. To the best of our knowledge, this is one of the first attempts in the literature to train ML to predict heatmap representation of incident-induced spatiotemporal impact, and speeding up the training via simulation. </jats:p>

Imbalance analysis of regional economic law laws incorporating improved PPO algorithms

“…In order to further address the high variance problem in the PPO algorithm, which is triggered by the difference in the state value function due to the difference in the probability of an agent taking an action, the dominance estimation is corrected with the help of the characterization capability of generative adversarial networks [24][25]. The generator network is used to generate instances that are infinitely similar to the true state value function based on the real environmental data of the input network.…”