A big data approach to cargo type prediction and its implications for oil trade estimation
Publication
, Journal Article
Li, Y; Bai, X; Wang, Q; Ma, Z
Published in: Transportation Research Part E: Logistics and Transportation Review
September 2022
Duke Scholars
Published In
Transportation Research Part E: Logistics and Transportation Review
DOI
ISSN
1366-5545
Publication Date
September 2022
Volume
165
Start / End Page
102831 / 102831
Publisher
Elsevier BV
Related Subject Headings
- Logistics & Transportation
- 3509 Transportation, logistics and supply chains
- 1507 Transportation and Freight Services
- 0103 Numerical and Computational Mathematics
- 0102 Applied Mathematics
Citation
APA
Chicago
ICMJE
MLA
NLM
Li, Y., Bai, X., Wang, Q., & Ma, Z. (2022). A big data approach to cargo type prediction and its implications for oil trade estimation. Transportation Research Part E: Logistics and Transportation Review, 165, 102831–102831. https://doi.org/10.1016/j.tre.2022.102831
Li, Yiliang, Xiwen Bai, Qi Wang, and Zhongjun Ma. “A big data approach to cargo type prediction and its implications for oil trade estimation.” Transportation Research Part E: Logistics and Transportation Review 165 (September 2022): 102831–102831. https://doi.org/10.1016/j.tre.2022.102831.
Li Y, Bai X, Wang Q, Ma Z. A big data approach to cargo type prediction and its implications for oil trade estimation. Transportation Research Part E: Logistics and Transportation Review. 2022 Sep;165:102831–102831.
Li, Yiliang, et al. “A big data approach to cargo type prediction and its implications for oil trade estimation.” Transportation Research Part E: Logistics and Transportation Review, vol. 165, Elsevier BV, Sept. 2022, pp. 102831–102831. Crossref, doi:10.1016/j.tre.2022.102831.
Li Y, Bai X, Wang Q, Ma Z. A big data approach to cargo type prediction and its implications for oil trade estimation. Transportation Research Part E: Logistics and Transportation Review. Elsevier BV; 2022 Sep;165:102831–102831.
Published In
Transportation Research Part E: Logistics and Transportation Review
DOI
ISSN
1366-5545
Publication Date
September 2022
Volume
165
Start / End Page
102831 / 102831
Publisher
Elsevier BV
Related Subject Headings
- Logistics & Transportation
- 3509 Transportation, logistics and supply chains
- 1507 Transportation and Freight Services
- 0103 Numerical and Computational Mathematics
- 0102 Applied Mathematics