Multi-view context awareness based transport stay hotspot recognizing

Abstract

During long-distance transporting for bulk commodities, the trucks need to stop off at multiple places for resting, refueling, repairing or unloading, called as transport stay hotspots (or Tshot for short). Massive waybills and their related trajectories accumulated by the freight platforms enable us to recognize Tshots and keep them updated constantly. But due to most of Tshots have varying sizes and are adjacent to each other, it is hard to pinpoint their locations precisely. In addition, to correctly annotate functional tags of Tshots that have fewer visiting trajectories is quite difficult. In this paper, we propose a Multi-view Context awareness based transport \(\underline{S}\)tay hotspot Recognition framework, called MCSR, consisting of location identification, feature extraction and functional tag annotation. To address the missed-detection issue in pinpointing adjacent Tshots having various sizes, we design a multi-view clustering based stay area merging strategy by incorporating the distance between road turn-off locations, the number of visiting trajectories with the similarity of visiting time distribution. Further, aiming at the issue of low annotating precision resulted by data scarcity, based on extracting behavioral features and attribute features from waybill trajectories, we leverage a time interval awareness self-attention network to extract semantic contextual features to assist in ensemble learning based annotation modeling. Experimental results on a large-scale logistics dataset demonstrate that our proposal can improve F-measure by an average of 14.76%, AIoU by an average of 12.89% for location identification, and G-mean by an average of 18.39% and mAUC by an average of 14.48% for functional tag annotation as compared to the baselines.