Identifying the cargo types of road freight with semi-supervised trajectory semantic enhancement

Abstract

Identifying road freight cargo types is crucial for regional economic interaction and transportation optimization. Existing methods primarily rely on manual labeling and the rule, neither of which can achieve automated semantic enhancement of large-scale road freight trajectories. Consequently, this study proposes a semi-supervised trajectory semantic enhancement method for identifying cargo types based on trajectory feature extraction and point-of-interest (POI) association. The raw trajectories are segmented and enriched with the closest POIs. The sample labeling method with POI semantic enhancement is then proposed using company registration information. Finally, the spatiotemporal and sequential features of labeled freight trips are extracted to build a self-training semi-supervised model for identifying the cargo type of road freight. Experimental studies on real trajectory data demonstrate superior accuracy and robustness compared to existing methods, with accuracy and F1 values reaching 81.4 and 0.77%, respectively. The proposed sample labeling method improves representativeness and universality, increasing accuracy by 7.8–14.4% and F1 value by 8.5–34.5% compared to the rule-based method. The semi-supervised model improves accuracy by 8.9% and F1 value by 29.1% compared to the supervised model when only 10.0% of samples were labeled. This method enables automatic and full-sample cargo type identification in real-world large-scale transportation systems.