Citation

Su T, Elena Renda M, Zhao J. Transp. Res. Rec. 2022; 2676(2): 118-131.

Copyright

(Copyright © 2022, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981211037240

PMID

unavailable

Abstract

For decades, transportation researchers have used survey data to understand the factors that affect travel-related choices. Nowadays, travel surveys lay the foundation of travel behavior analysis for transportation modeling, planning, and policy-making. The development of information technology for urban sensing has enabled substantial improvements to be made in survey-elicited and passive mobility data collection. Actively collected and passive data are very different, and being able to compare and integrate them could allow stakeholders to achieve a greater understanding of human mobility. The comparison between survey self-reported travel behavior and actual travel behavior revealed by urban and mobile systems provides us with the opportunity to find potential discrepancies. Previous work has examined these discrepancies mostly at the population level. An individual-level investigation of these discrepancies could provide many benefits, from increasing our understanding of survey and passive data accuracy and collection, to designing personalized transportation services. In this study, the discrepancies between self-reported and observed travel behavior are analyzed at both the individual and aggregated level by utilizing the available mobility data, namely, survey-based commuting diaries and passive mobility records. We propose a group of discrepancy metrics for commuting activities for which we have available and comparable data, and apply the framework to an empirical analysis at the Massachusetts Institute of Technology in Cambridge, U.S.A. Our results show that survey-elicited commuting diaries are quite reliable when examining overall commuting trends, whereas passive mobility data are more suitable for investigating individual-level commuting behavior. Furthermore, we identify the association between discrepancies in commuting behavior and certain individual characteristics, for example, employee type and age.

Language: en

Keywords

planning and analysis; public transportation; transformative trends in transit data; travel survey methods; travel surveys