Identifying the cargo types of road freight with semi-supervised trajectory semantic enhancement
文献类型：期刊论文
| 作者 | Zhao, Yibo1,2; Cheng, Shifen1,2; Zhang, Beibei1,2; Lu, Feng1,2,3,4 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2023-11-28 |
| 页码 | 22 |
| 关键词 | Trajectory mining road freight transportation cargo type identification semantic trajectory semi-supervised learning |
| ISSN号 | 1365-8816 |
| DOI | 10.1080/13658816.2023.2288116 |
| 通讯作者 | Cheng, Shifen(chengsf@lreis.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | GPS ; LOGISTICS |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001152293700001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202490]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Cheng, Shifen |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Fuzhou Univ, Acad Digital China, Fuzhou, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Yibo,Cheng, Shifen,Zhang, Beibei,et al. Identifying the cargo types of road freight with semi-supervised trajectory semantic enhancement[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2023:22. |
| APA | Zhao, Yibo,Cheng, Shifen,Zhang, Beibei,&Lu, Feng.(2023).Identifying the cargo types of road freight with semi-supervised trajectory semantic enhancement.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,22. |
| MLA | Zhao, Yibo,et al."Identifying the cargo types of road freight with semi-supervised trajectory semantic enhancement".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2023):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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