Estimating the Impact of COVID-19 on Travel Demand in Houston Area Using Deep Learning and Satellite Imagery

2026-03-28

AI Summary

This study assesses the impact of the COVID-19 pandemic on travel demand in the Houston metropolitan area by integrating high-resolution satellite imagery with a deep learning–based vehicle detection model built upon Detectron2 and Faster R-CNN. The approach enables non-intrusive vehicle counting at representative locations—including university campuses, shopping malls, and community plazas—to quantify changes in mobility by comparing 2019 and 2020 data. This work presents the first integration of satellite remote sensing and deep learning for large-scale, efficient monitoring of urban travel patterns and associated economic activity. Results indicate an average 30% reduction in vehicle counts across selected sites in 2020 relative to 2019, demonstrating the effectiveness and practical potential of this methodology for estimating travel demand.

Abstract

Considering recent advances in remote sensing satellite systems and computer vision algorithms, many satellite sensing platforms and sensors have been used to monitor the condition and usage of transportation infrastructure systems. The level of details that can be detected increases significantly with the increase of ground sample distance (GSD), which is around 15 cm - 30 cm for high-resolution satellite images. In this study, we analyzed data acquired from high-resolution satellite imagery to provide insights, predictive signals, and trend for travel demand estimation. More specifically, we estimate the impact of COVID-19 in the metropolitan area of Houston using satellite imagery from Google Earth Engine datasets. We developed a car-counting model through Detectron2 and Faster R-CNN to monitor the presence of cars within different locations (i.e., university, shopping mall, community plaza, restaurant, supermarket) before and during the COVID-19. The results show that the number of cars detected at these selected locations reduced on average 30% in 2020 compared with the previous year 2019. The results also show that satellite imagery provides rich information for travel demand and economic activity estimation. Together with advanced computer vision and deep learning algorithms, it can generate reliable and accurate information for transportation agency decision makers.

Problem

• COVID-19
• travel demand
• satellite imagery
• Houston
• impact estimation

Innovation

• satellite imagery
• deep learning
• vehicle detection
• travel demand estimation
• Faster R-CNN