Enhancing Urban Flow Maps via Neural ODEs
Authors: Fan Zhou, Liang Li, Ting Zhong, Goce Trajcevski, Kunpeng Zhang, Jiahao Wang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations on two realworld datasets demonstrate that FODE significantly outperforms several baseline approaches. |
| Researcher Affiliation | Academia | 1School of Information and Software Engineering, University of Electronic Science and Technology of China 2Iowa State University, Ames IA 3University of Maryland, College Park MD |
| Pseudocode | Yes | Algorithm 1 Gradient calculation in FODE. |
| Open Source Code | Yes | We note that the details of other network settings are described in the source-implementation2. 2https://github.com/Anewnoob/FODE |
| Open Datasets | Yes | We evaluate all the methods using two real-world urban flow datasets: (1) Taxi BJ [Liang et al., 2019] a taxi GPS data including taxi flows from July 1, 2014 to October 31, 2014; and (2) Bike NYC collected from an open website1 which contains data from January 1, 2019 to June 30, 2019. 1https://www.citibikenyc.com/system-data |
| Dataset Splits | No | The paper mentions performing a test on the validation set during training, but does not provide specific split percentages, counts, or a method for reproducing the validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam' optimizer and 'Dopri5 numerical method' but does not provide version numbers for these or any other software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | Adam [Kingma and Ba, 2014] is adopted to train FODE with batch size 16 and learning rate e 4. We leverage Dopri5 numerical method, which can adaptively choose the step size, as ODESovle in FODE. FODE consists of 128 channels and 1 ODE block. We also present a simplified version S-FODE which contains 64 channels while other components are the same as FODE. During training, we halve the learning rate and perform a test on the validation set every 20 epochs. |