GMDNet: A Graph-Based Mixture Density Network for Estimating Packages' Multimodal Travel Time Distribution

The authors of this paper (Mao et al. 2023) acknowledge that although it referred to an earlier paper already presented and published in ICML-21 (Errica, Bacciu, and Micheli 2021), it insufficiently acknowledged the extent to which it incorporated and made extensive use of techniques therein. The authors wish to apologize for this omission. The main novel contributions of this paper are:

• Accurately estimating packages' travel time distribution by analyzing influencing factors in the travel routes and logistics networks.

• Extending graph data to incorporate influencing factors within the logistics network.

• Integrating mutual correlations in sequence data to estimate multimodal travel time distribution. This clarification is the culmination of a thorough review by the AAAI publications committee, who commissioned two independent reviewers whose expert advice contributed to the decision making process.