POPAyI: Muscling Ordinal Patterns for Low-Complex and Usability-Aware Transportation Mode Detection

Detecting transportation modes’ usability in spatiotemporal urban trajectories can provide valuable insights into the mobility preferences of urban populations, helping epidemic prevention and urban quality-of-life improvement. With this goal, we introduce polar ordinal patterns with amplitude information (POPAyI), a strategy that bases its design on the ordinal pattern (OP) transformation applied to mobility-related time series. POPAyI can quantify time-series dynamics with a low-complex cost, muscling time series’ characteristics without the need for high computational and methodological complexities as the current machine learning (ML) and deep learning (DL) literature. POPAyI uses polar representation and captures amplitude information in time series, bringing the multivariate capability to the standard 1-D OP transformation. Our experiments show that POPAyI: 1) perfectly adapts to multidimensional mobility time series and natural nonlinear mobility behavior and 2) presents consistent detection results in any considered number of transportation mode’s classes with efficiency in terms of storage and computation complexity, using fewer features than ML approaches and computational resources than DL methods, e.g., reaching 10000 fewer parameters than a lightweight DL approach while increasing by 3% the F1-score.