Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
Authors: Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Hui Xiong4617-4625
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments have been conducted on two real-world datasets, NYC Citi Bike and NYC Taxi, and the results demonstrate the superiority of our model over the state-of-the-art ones. |
| Researcher Affiliation | Academia | 1SKLSDE and BDBC Lab, Beihang University, Beijing 100083, China, 2Department of Computer Science, University of Central Florida, FL 32816, USA, 3Management Science and Information Systems Department, Rutgers University, NJ 07102, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes algorithms in paragraph form or mathematical equations. |
| Open Source Code | Yes | The source code is available 1. https://github.com/Essaim/CGCDemandPrediction |
| Open Datasets | Yes | Experiments are conducted on two real-world datasets collected from NYC Open Data. The two datasets contain order records of taxi and bike in NYC. NYC Citi Bike2: This dataset includes the NYC Citi bike orders of people daily using. [...] NYC Taxi3: This dataset consists of 35 million taxicab trip records in New York from April 1st, 2016 to June 30th, 2016. (...) 2https://www.citibikenyc.com/system-data 3https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page |
| Dataset Splits | Yes | Among the last four weeks, the first two are used for validation, and the last two are for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It only mentions the PyTorch framework. |
| Software Dependencies | No | The paper mentions 'Adam algorithm (Kingma and Ba 2014)' and 'PyTorch framework', but it does not specify version numbers for PyTorch or any other libraries/solvers. |
| Experiment Setup | Yes | The time step length is set to half an hour, such as 0 : 00am to 0 : 30am, 0 : 30am to 1 : 00am, 1 : 00am to 1 : 30am. (...) The feature dimension D is 2, representing the pick-up demand and drop-off demand. The historical demand length P is set to 12 and the prediction length Q is 12, too. In the adjacency matrix generation, to avoid the influence of validation and testing data, we employ the entire training dataset to learn stations representations. That is to say, the first time step of generating adjacency matrix ta is 0, and the length τ is 3,011 (the length of training set). The dimension of station feature ξ is set to 20. In CGC, the number of stacked convolution layers M is 3. We use Equation (14) as final convolution layer with diffusion steps K = 3. The dimension of two adaptive matrices L is 50. The hidden states dimension β is set to 25. Learning rates for NYC Citi Bike and NYC Taxi datasets are 0.0005 and 0.0015. For training stability, we initialize the weight W as identity matrix and bias b as 0 in coupled mapping. All methods are optimized by Adam algorithm (Kingma and Ba 2014). |