Dynamic Time Warping-based imputation of long gaps in human mobility trajectories

TL;DR

This paper introduces DTWBMI, a novel imputation method based on dynamic time warping, to accurately fill long gaps in human mobility trajectories, improving data completeness for travel behavior analysis.

Contribution

The paper presents a new DTWBMI method that effectively imputes long missing segments in mobility data, outperforming traditional methods for longer gaps and larger datasets.

Findings

DTWBMI outperforms other methods for long gaps in mobility data.

Linear interpolation is better for short gaps with limited data.

Two optimized versions of DTWBMI are proposed and tested.

Abstract

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.