Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Abstract

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground-truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the data set requirements. This article proposes an LDV trip detection model as a supplement to current travel mode detection methods for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique—one-class support vector machines—and a novel exhaustive feature extraction technique on continuous time series data [i.e., global positioning system (GPS) speed profiles] for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of pre-existing in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. This article also discusses performance comparison with regular machine learning approaches, the model's robustness, and the potential to extend the proposed model to multimodal trip prediction.