SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 7 showcases the performance of the synthetic dataset in the context of travel time estimation for Xi'an city. Again, TEMP, XGBoost, WDR, and DeepTTE are employed for comparison, and the results are evaluated based on RMSE, MAE, and MAPE metrics. Although the absolute performance figures change due to the distinct characteristics of the new city data, the consistency between the original and synthetic datasets remains robust across the metrics and models. The difference ratio remains in a similar range, indicating flexibility and reliability of the SYN-XI'AN dataset when applied to different cities. Noteworthy is the reversed performance difference in RMSE for the WDR model, showing a  $10.03\%$  discrepancy. This divergence can be attributed to the unique spatial-temporal characteristics of the different city data, indicating the need for context-specific fine-tuning when transferring models between cities. Nonetheless, the synthetic dataset still provides a reasonable approximation for most models and metrics, demonstrating its generalizability across different urban environments. In summary, these findings further reinforce the utility of our synthetic dataset, establishing its value not only for travel time estimation but also for the broader scope of urban mobility research in various cities.
C Additional Visualization for Use Cases
This section shows the visualizations of use case results (Section 5 and Appendix B).
Figure 8: Performance metrics visualization for traffic demand prediction (Chengdu).
Figure 9: Performance metrics visualization for travel time estimation (Chengdu).
Figure 10: Performance metrics visualization for traffic demand prediction (Xi'an).
Figure 11: Performance metrics visualization for travel time estimation (Xi'an).