ABSTRACT

COVID-19, a respiratory virus violently spread worldwide, has deeply affected people’s daily life and travel behaviors. We adopted an autoregressive distributed lag model to analyze changes in travel patterns in Houston, Texas during COVID-19. The results indicated that visit patterns and changes in COVID-19 cases a week prior heavily influence the following week’s behaviors. Additionally, unemployment claims, median minimum dwell time, and workplace visit activity played a major role in predicting total foot traffic. Notably, transit systems have seen an overall decrease in usage but were not significant in estimating total foot traffic. This model showcased a unique method of quantifying and analyzing travel behaviors in Houston in response to COVID-19.

摘要

COVID-19是一种在全球范围内猛烈传播的呼吸道病毒，已经深刻影响了人们的日常生活和出行行为。我们采用自回归分布式滞后模型来分析 COVID-19 期间德克萨斯州休斯顿的旅行模式变化。结果表明，一周前的访问模式和 COVID-19 病例的变化会严重影响下一周的行为。此外，失业申请、最短停留时间中位数和工作场所访问活动在预测总人流量方面发挥了重要作用。值得注意的是，交通系统的使用量总体下降，但在估计总人流量方面并不显着。该模型展示了一种独特的方法，可以量化和分析休斯顿应对 COVID-19 的旅行行为。