Estimating Latent People Flow without Tracking Individuals
Authors: Yusuke Tanaka, Tomoharu Iwata, Takeshi Kurashima, Hiroyuki Toda, Naonori Ueda
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the effectiveness of our model on real-world datasets, pedestrian location logs from large-scale exhibition halls, and bike trip data and taxi trip data in New York City. and Table 3 shows NMAE L1 of the proposed model, CFDM, Popularity and Uniform. For all datasets, the proposed model performed better than the other methods, and the differences between our model and CFDM were significant (t-test, p < 0.01). |
| Researcher Affiliation | Collaboration | 1 NTT Service Evolution Laboratories, Kanagawa 239-0847, Japan 2 NTT Communication Science Laboratories, Kyoto 619-0237, Japan 3 RIKEN Center for AIP, Tokyo 103-0027, Japan |
| Pseudocode | Yes | Algorithm 1: Inference procedure for our model. |
| Open Source Code | No | The paper does not state that its source code is publicly available or provide a link to a repository. |
| Open Datasets | Yes | To additionally validate the performance of our model, we used two public datasets, bike trip data 2 and taxi trip data 3 in New York City. 2https://www.citibikenyc.com/system-data 3http://www.nyc.gov/html/tlc/html/about/trip record data.shtml |
| Dataset Splits | No | The paper describes a validation procedure for lambda using "prediction performance in the training data" but does not provide explicit training/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | For our model, hyperparameter λ was set to the best value based on the validation procedure described in Section 5; λ was chosen from 0.1, 0.2, 0.5, 1, 2, and 5. We used a Rayleigh distribution for modeling travel duration distribution as shown in Section 4. |