论文标题
基于代理的仿真模型和深度学习技术，以评估和预测Covid-19的运输趋势
Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19
论文作者
论文摘要
COVID-19的大流行影响了旅行行为和运输系统的运营，城市正在努力应对哪些政策可以有效地通过社会疏远而置于跨性别的阶段重新开放。该版本的白皮书更新了旅行趋势，并强调了基于代理的仿真模型的结果，以预测拟议的分阶段重新开放策略的影响。它还引入了一种实时视频处理方法，可以通过城市街道上的相机来衡量社交距离。
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. This edition of the white paper updates travel trends and highlights an agent-based simulation model's results to predict the impact of proposed phased reopening strategies. It also introduces a real-time video processing method to measure social distancing through cameras on city streets.
