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.

越来越强调的研究领域是对随机交通事故（如撞车或道路封闭）引起的非经常性交通动态的短期交通状况的预测。这项研究提出了一个预测框架，该框架着重于训练机器学习（ML）模型来预测与事件相关的速度热图。热图包含理想的信息，该信息描述了事件诱发的冲击的时空特征，并且是ML模型可以理解和预测的合适对象。由于现实世界中事件数据的稀疏性，我们提出了一种仿真方法来快速扩展训练数据集，从而加快了模型训练过程。使用条件深度卷积生成对抗网络预测速度热图，并使用介观动态交通分配模型DynusT生成许多训练数据。评估显示，所提出的模型以预测和实际热图之间的80.19％相似性捕获像素值的色调和空间分布。据我们所知，这是文献中训练ML来预测事件引起的时空影响的热图表示并通过模拟加快训练的首次尝试之一。