How comparable are origin-destination matrices estimated from automatic fare collection, origin-destination surveys and household travel survey? An empirical investigation in Lyon.

• An heterogeneous corpus of data sources is now available to estimate travel demand. • We compare three independent data sources to estimate public transit OD matrices. • Automatic fare collection data is not error-free. • Household travel survey may significantly underestimate the volume of trips. • Better understanding of the available data sources and the potential trade-offs. Origin–destination (OD) matrices are one of the key elements in travel behaviour analysis. For decades, transportation researchers have mostly used data obtained by active solicitation such as surveys to construct these matrices but new data sources like automatic fare collection (AFC) are now available and can be used to measure OD flows. As a result, a more heterogeneous corpus of data sources is now available to estimate travel demand. However, little research examines how comparable the estimated demands may be. In this paper, three data sources namely a household travel survey, a large scale origin–destination survey and entry only automated fare collection are processed to derive typical weekday public transit OD trip matrices. Various elements of the resulting matrices are then compared. While all the matrices share some common characteristics, there are also substantial differences that must be addressed. AFC data is not error-free and needs to be supplemented with data from other sources to construct a representative OD trip matrix. This is because not all destinations can be inferred, the smart card penetration rate is far less than 100% and fare evasion cannot be ignored. Our empirical results suggest that scaling an AFC matrix with automated passenger counts may be a viable solution. The results also indicate that the household travel survey significantly underestimates the volume of public transit trips compared to the other sources. The findings of this research contribute to a better understanding of the available data sources for public transit demand estimation. They can help practitioners to improve the quality and accuracy of OD matrices. [ABSTRACT FROM AUTHOR]