MetaER-TTE: An Adaptive Meta-learning Model for En Route Travel Time Estimation
Authors: Yu Fan, Jiajie Xu, Rui Zhou, Jianxin Li, Kai Zheng, Lu Chen, Chengfei Liu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct comprehensive experiments to demonstrate the superiority of Meta ER-TTE. We conduct extensive experiments on two real-world datasets to verify the effectiveness of our proposed model. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Soochow University 2Swinburne University of Technology 3Deakin University 4University of Electronic Science and Technology of China 20205227013@stu.suda.edu.cn, xujj@suda.edu.cn, {rzhou, luchen,cliu}@swin.edu.au, jianxin.li@deakin.edu.au, zhengkai@uestc.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We perform our experiments on two real trajectory datasets, Beijing and Porto. Porto dataset is publicly available with 737063 trajectories generated from Jul 1st, 2013 to Jul 1st, 2014. |
| Dataset Splits | Yes | For Beijing dataset, 80% of the trajectories are used to train the model and the remaining 20% are used to test. For Porto dataset, we select the last two months for testing the model and the remaining ten months for training. We choose hyperparameters by conducting meta-train using different hyperparameters and select the parameters with the best performance, thus we do not set the validation set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | The time slot is set to 30 minutes to avoid the absence of historical traffic conditions due to the sparse data. Other settings of our base model are the same as Const GAT [Fang et al., 2020]. For each trajectory, we split it into 30% as the traveled route and 70% as the remaining route, the average numbers of the road segments in the traveled and remaining parts of trajectories are 15, 34 in Beijing dataset, and 14, 31 in Porto dataset. The number of clusters is set as 3 for two datasets according to the comparisons of different cluster numbers in Figure 2. The initial learning rate of global update is set as 0.0001. |