DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation
Authors: Xiaowei Mao, Yan Lin, Shengnan Guo, Yubin Chen, Xingyu Xian, Haomin Wen, Qisen Xu, Youfang Lin, Huaiyu Wan
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two realworld datasets demonstrate the superiority of our proposed method. |
| Researcher Affiliation | Academia | 1School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China 2Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence , Beijing, China 3Aalborg University, Aalborg, Denmark 4Carnegie Mellon University, Pittsburgh, USA |
| Pseudocode | No | The paper describes methods and mathematical formulations but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/maoxiaowei97/Duty TTE |
| Open Datasets | No | The paper uses "two real-world datasets" named Chengdu and Xi'an, and provides their statistics in Table 2, but does not offer concrete access information (e.g., links, DOIs, or specific citations to publicly available versions) for these datasets. |
| Dataset Splits | No | The paper does not explicitly provide specific percentages, sample counts, or detailed methodologies for training, validation, and testing dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | In Figure 4 (a), we set the value of k and the number of experts as follows: c1 : k = 1, C = 8, c2 : k = 2, C = 8, c3 : k = 4, C = 8, c4 : k = 6, C = 8. In Figure 4 (b), we configure the policy loss function with the following hyper-parameters: c5 : ω = 1, β = 50, γ = 1, c6 : ω = 1, β = 100, γ = 1, c7 : ω = 1, β = 50, γ = 0.5, c8 : ω = 0, β = 0, γ = 0. |