Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19
This addresses transportation planning challenges for cities during the pandemic, but appears incremental as it updates existing work with new data and methods.
The paper tackles predicting transportation trends during COVID-19 by using an agent-based simulation model to evaluate phased reopening strategies and introducing a real-time video processing method to measure social distancing from street cameras.
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.