2024/10/21 by Danielle McCool, Peter Lugtig, McCool, Danielle +3
Computer Science · Social Sciences · #62-08 #Artificial intelligence #Computer science #Data Management and Algorithms #Dynamic time warping #Econometrics #Economics #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Image warping #Imputation (statistics) #Machine learning #Methodology (stat.ME) #Missing data #Urban Transport and Accessibility
paper · pdf · doi:10.48550/arxiv.2410.16096
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Individual mobility trajectories are difficult to measure and often incur long periods of missingness. Aggregation of this mobility data without accounting for the missingness leads to erroneous results, underestimating travel behavior. This paper proposes Dynamic Time Warping-Based Multiple Imputation (DTWBMI) as a method of filling long gaps in human mobility trajectories in order to use the available data to the fullest extent. This method reduces spatiotemporal trajectories to time series of particular travel behavior, then selects candidates for multiple imputation on the basis of the dynamic time warping distance between the potential donor series and the series preceding and following the gap in the recipient series and finally imputes values multiple times. A simulation study designed to establish optimal parameters for DTWBMI provides two versions of the method. These two methods are applied to a real-world dataset of individual mobility trajectories with simulated missingness and compared against other methods of handling missingness. Linear interpolation outperforms DTWBMI and other methods when gaps are short and data are limited. DTWBMI outperforms other methods when gaps become longer and when more data are available.