Field notes from the travel app frontier

This dissertation addresses a central methodological limitation of smartphone-based travel surveys (SBTS): systematic missingness in passively collected location data. While smart surveys that combine self-reported information with sensor-based measurements offer substantial advantages over conventional travel diaries—including reduced recall bias, higher trip detection rates, and lower respondent burden—their analytical validity depends critically on how incomplete trajectory data are handled. The research investigates three interrelated questions: (1) what mechanisms generate missing location data in SBTS, (2) how missingness affects commonly used mobility indicators, and (3) whether imputation methods can produce complete datasets suitable for reliable mobility analysis. These questions are examined using empirical data from a large-scale field test conducted by Statistics Netherlands in 2018, in which participants used the Tabi travel app to record and annotate their movements, complemented by extensive simulation studies. Empirical analysis shows that missing data are structural rather than incidental. On average, participants recorded approximately one-third of the intended daily observation time, with data completeness varying systematically by device type, operating system behavior, and user characteristics. Comparisons with the national travel diary survey (ODiN) indicate that smartphone data capture more trips—particularly short and incidental movements—but produce biased estimates of total travel distance when missingness is ignored. These findings demonstrate that SBTS introduce new forms of measurement heterogeneity that are not addressed by conventional data-processing practices. Simulation experiments quantify the conditions under which missing data become analytically problematic. Short gaps in location data (under approximately ten minutes) can be addressed using linear interpolation with limited bias across standard mobility metrics, including total distance traveled and radius of gyration. In contrast, longer gaps produce non-linear bias amplification, with naive interpolation substantially underestimating travel behavior, especially when missingness is temporally clustered. To address this limitation, the dissertation introduces Dynamic Time Warping–Based Multiple Imputation (DTWBMI), a trajectory-specific imputation framework designed to reconstruct long gaps in mobility data. DTWBMI matches incomplete daily trajectories to reference days based on temporal movement similarity and generates multiple plausible realizations to propagate uncertainty. Two variants are developed for data-rich and data-poor contexts, respectively. Evaluation results show that DTWBMI substantially outperforms linear interpolation for long gaps, with the low-information variant exhibiting unexpectedly robust performance. The proposed methods are integrated into a hierarchical imputation framework that combines interpolation, DTWBMI, and multiple imputation depending on gap duration. Application to real SBTS data yields stable mobility estimates with reduced bias and uncertainty compared to common practices such as listwise deletion. Overall, the dissertation reframes missing data in SBTS from a prohibitive flaw into a manageable methodological challenge, providing empirically validated tools that support the integration of smartphone-based mobility measurement into transport research and official statistics.