Investigation of crowdshipping delivery trip production with real-world data
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DOI: 10.1016/j.tre.2020.102106
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Cited by:
- Martin W.P Savelsbergh & Marlin W. Ulmer, 2022. "Challenges and opportunities in crowdsourced delivery planning and operations," 4OR, Springer, vol. 20(1), pages 1-21, March.
- Hou, Ting & Zhang, Wen, 2021. "Optimal two-stage elimination contests for crowdsourcing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
- Wang, Haibo & Alidaee, Bahram, 2023. "White-glove service delivery: A quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
- Huang, Zhiwen & Li, Tong & Huang, Kexin & Ke, Hanbing & Lin, Mei & Wang, Qiuwang, 2022. "Predictions of flow and temperature fields in a T-junction based on dynamic mode decomposition and deep learning," Energy, Elsevier, vol. 261(PA).
- Bathke, Henrik & Hartmann, Evi, 2021. "Accepting a crowdsourced delivery - A choice-based conjoint analysis," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Adapting to the Future: Maritime and City Logistics in the Context of Digitalization and Sustainability. Proceedings of the Hamburg International Conf, volume 32, pages 65-95, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
- Xiao, Haohan & Xu, Min & Wang, Shuaian, 2023. "A game-theoretic model for crowd-shipping operations with profit improvement strategies," International Journal of Production Economics, Elsevier, vol. 262(C).
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Keywords
Crowdshipping (CS); Delivery trip production forecasting; Spatial and temporal correlation; Discrepancies in CS demand and supply; Spatio-temporal deep learning (DL) method;All these keywords.
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