Transfer learning-based approach for fine-grained travel mode identification on mobile phone signalling data

Abstract

Travel Mode Identification (TMI) using mobile phone signalling data (MSD) has garnered significant attention. Previous work primarily focused on coarse-grained identification (e.g., public vs. private transport) due to MSD's low spatial accuracy, irregular temporal features, and lack of labelled data. To tackle these challenges, this paper proposes a novel fine-grained TMI framework with transfer learning. It consists of two stages, where the first stage applies heuristic rules fused with GIS information to identify metro. The second utilizes a Multi-Head Attention-Conditional Random Field (MACRF) model with transfer learning for point-level identification of walk, bike, bus, and car. Novel features are designed to counter spatio-temporal uncertainty. Extensive evaluation with ground truth in Hangzhou shows the approach significantly boosts accuracy over baselines via transfer learning. Moreover, comprehensive analysis offers valuable insights. The framework exhibits great generalisation capability and holds potential for large-scale travel demands survey in cities.