- The paper appears to require daily generation of the dynamic road network topology using the tree-based adjacency matrix generation algorithm. The efficiency of this process remains unclear. Additionally, since the topologies undergo minimal changes between consecutive days, and substantial information is shared across these days, it raises the question of whether specialized algorithms are available to accelerate this topology generation.
- The authors present performance results only for two districts, D06 and D11. It is recommended to extend the reporting to include experimental results from the remaining seven districts.
- There is an inconsistency in the layout of the document: Figure 5 referred to on line 215 of Page 4, yet it is located on Page 7.
- The caption for Figure 7 is incorrect, and should be corrected to "Edge Dynamics" from "Node Dynamics".
- It is recommended that some recent related studies be discussed in the paper, particularly focusing on their performance with this dataset.
[1] UniST: A Prompt-Empowered Universal Model for Urban ST Prediction. KDD2024.
[2] Fine-Grained Urban Flow Prediction. WWW2021.
[3] When Transfer Learning Meets Cross-City Urban Flow Prediction: Spatio-Temporal Adaptation Matters. IJCAI2022.
[4] Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction. AAA2023.
- The caption for Figure 7 is incorrect, and should be corrected to "Edge Dynamics" from "Node Dynamics".