Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction
Authors: Samuel S. Sohn, Seonghyeon Moon, Honglu Zhou, Mihee Lee, Sejong Yoon, Vladimir Pavlovic, Mubbasir Kapadia
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In order to evaluate the scalability of models to large and complex environments, which the only existing LTCFP dataset is unsuitable for, a new synthetic crowd dataset with both real and synthetic environments has been generated. In its nascent state, LTCFP has much to gain from our key contributions. |
| Researcher Affiliation | Academia | 1Rutgers University, USA 2The College of New Jersey, USA {sss286, sm2062, hz289, ml1323, vladimir, mk1353}@cs.rutgers.edu, yoons@tcnj.edu |
| Pseudocode | No | No pseudocode or algorithm blocks were found. The methodology is described using text and diagrams. |
| Open Source Code | Yes | The Supplementary Materials, dataset, and code are available at sssohn.github.io/Geo Interact Net. |
| Open Datasets | Yes | The Supplementary Materials, dataset, and code are available at sssohn.github.io/Geo Interact Net. [...] The 2 synthetic datasets consists of 8,000 total training and 2,400 total testing crowd scenarios with thousands of unique synthetic environments. |
| Dataset Splits | No | The paper specifies '8,000 total training and 2,400 total testing crowd scenarios' for synthetic datasets and that 'both real datasets for testing only'. It does not explicitly mention or specify a validation split or set of its own, only training and testing. |
| Hardware Specification | Yes | A machine with an Intel Core i9-9960X 3.10 GHz, 64GB RAM, and an NVIDIA Ge Force RTX 2080 Ti 11GB was used for all training and testing. |
| Software Dependencies | No | The paper mentions 'Adam optimization' and 'stochastic gradient descent' and specific deep learning models (U-Net, Attention U-Net, Seg Net, CAGE) but does not provide specific version numbers for any software libraries (e.g., PyTorch, TensorFlow, or Python versions). |
| Experiment Setup | Yes | Adam optimization [Kingma and Ba, 2014] was used for training U-Net, Attention U-Net, and GINet, while stochastic gradient descent was used for CAGE and Seg Net (with momentum = 0.9). Prior to training, the data was shuffled, and the batch size was set to 4. All models were then trained exclusively on both synthetic datasets for 100 epochs with a learning rate of 0.01 from {0.1, 0.01, 0.001}, which performed best across models. The loss function was set to Mean Absolute Error (MAE). |