Representing Urban Functions through Zone Embedding with Human Mobility Patterns
Authors: Zijun Yao, Yanjie Fu, Bin Liu, Wangsu Hu, Hui Xiong
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments with real-world urban datasets of New York City. Experimental results demonstrate the effectiveness of the proposed embedding model to represent urban functions of zones with human mobility data. |
| Researcher Affiliation | Collaboration | Zijun Yao1, Yanjie Fu2, Bin Liu3, Wangsu Hu1, Hui Xiong1 1Rutgers University 2Missouri University of Science and Technology 3IBM Thomas J. Watson Research Center |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | We use the trip records of yellow taxi from NYC taxi & limousine commission2 to obtain citywide human mobility patterns. (...) The second dataset is the zone data. We use the city zones designed by US Census Bureau3 for zone embedding learning. (...) The last dataset is the Foursquare check-in data formulated by the work in [Yang et al., 2015] |
| Dataset Splits | No | Finally we obtain 33,842,934 trips as our training data. The paper mentions this as 'training data' but does not specify a separate validation split or explicit cross-validation setup for the model training itself. It uses a separate 'land use dataset' as ground truth for evaluating clustering performance, but this is not a validation split from the primary mobility data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For experimental setup, we empirically set embedding dimension D = 50. Gravity matrices Gwd and Gwe are calculated with βwd = 0.4674 and βwe = 0.3881. |