A Comprehensive Data Fusion to Evaluate the Impacts of COVID-19 on Passenger Travel Demands: Application of a Core-Satellite Data Collection Paradigm

Abstract The COVID-19 pandemic has altered travel patterns in cities across the world. Previous studies have found that travel choices during the pandemic are affected by attitudes and perceptions of risk in addition to transportation system level-of-service attributes. However, traditional travel demand models often rely on household travel survey data, which rarely include information on attitudinal factors. Conversely, specialized surveys are often lengthy, so they offer the ability to collect detailed attitudinal information but suffer from limited sample sizes. This study demonstrates the feasibility of fusing a “core” household travel survey with three specialized “satellite” surveys to evaluate the impacts of COVID-19 on passenger travel demand in the Greater Toronto Area (GTA). The study uses a non-parametric implicit data fusion method to generate multiple synthetic datasets that contain observed travel diaries and socioeconomic attributes of the trip-makers from the core survey, along with imputed attitudinal statements based on the satellite surveys. The results highlight the ability of the method to sufficiently reproduce the distribution of the attitudinal variables and the ability of the imputed variables to support the estimation of an advanced econometric model. The proposed method can reduce the risk of potential biases in the imputed data that can adversely impact subsequent data analysis. This method can be used to capitalize on the benefits of specialized surveys while still being able to utilize data from large-scale household travel surveys.