The last decade has seen a growing interest in semi-passive travel diaries. These diaries are characterized, in contrast to fully-passive ones, by the active validation and correction by the participants of automatically-generated trips. Albeit promising and with important benefits in terms of cost, scalability, and trip-recall quality, these diaries still face challenges resulting from data collection errors and imperfect validation by users. In an aim to become an integral part of Household Travel Surveys, it is essential to develop a method for enhancing the quality of these diaries, increasing their reliability, correctness, and usability in further mobility analyses, however, such methodology has yet to be discussed in the literature. In long-duration studies one can prioritize quality over quantity, due to the sheer amount of data, to yield a highly meaningful sample. In this paper, we present a data quality enhancement method for large-scale long-duration semi-passive travel diaries that targets erroneous records (noise, or from poor validation), enriches the data (e.g., trip and tour detection) and adds supplementary information. We demonstrate its benefits when applied to a one-year study with over a thousand participants. Furthermore, we share our experience working with this unique data and provide insights about the participants' behavior in validation and app interaction that could be of interest for the design of future studies. The output of the proposed method is a meaningful design agnostic dataset; hence facilitating further mobility data analyses. We further recommend that future studies promote active correction and validation by the user.     «

The last decade has seen a growing interest in semi-passive travel diaries. These diaries are characterized, in contrast to fully-passive ones, by the active validation and correction by the participants of automatically-generated trips. Albeit promising and with important benefits in terms of cost, scalability, and trip-recall quality, these diaries still face challenges resulting from data collection errors and imperfect validation by users. In an aim to become an integral part of Household Tr...     »