arXiv:2606.00979v1 Announce Type: new Abstract: Accurate Estimated Time of Arrival (ETA) prediction on checkout page is crucial in instant logistics for enhancing user satisfaction, optimizing dispatching, and controlling operational costs. In international on-demand delivery platforms, where ETA data originates from diverse countries or regions with different patterns, multi-domain modeling is of great importance and has been widely adopted. However, existing methods still face three critical challenges in real-world deployment. First, current multi-domain models struggle to generalize to com
The increasing complexity and global scale of logistics, particularly in instant delivery platforms, necessitates more robust and adaptable AI solutions for ETA prediction.
Improved cross-domain ETA prediction directly impacts operational efficiency, customer satisfaction, and profitability for logistics and e-commerce companies operating across diverse geographic and cultural contexts.
This framework offers a unified approach to overcome generalization challenges in multi-domain ETA modeling, potentially leading to more accurate and reliable logistics operations globally.
- · Logistics platforms
- · E-commerce companies
- · AI/ML researchers
- · Logistics companies with inefficient ETA systems
- · Legacy AI solutions
More accurate delivery times for consumers enhance user experience and loyalty.
Optimized delivery routes and resource allocation reduce operational costs and environmental impact.
Enhanced logistical predictability could enable new business models reliant on precise timing and global delivery capabilities.
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Read at arXiv cs.LG