| Replication package explanatory file
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| Journal title: Communications in Transportation Research (COMMTR)
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| Manuscript title: Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
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| Manuscript ID: [to be filled after ScholarOne submission]
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| Authors: [to be filled in author order]
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| Replication package access
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| The complete replication package is deposited on Hugging Face Datasets (processed data under CC0; code under MIT) and mirrored on GitHub. After ETS-Data assigns a DOI, replace the placeholder below and also update the short summary in the manuscript (before Acknowledgements).
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| Hugging Face: https://huggingface.co/datasets/tjtrans/FORESEE
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| ETS-Data: https://doi.org/10.26599/ETSD.XXXX.XXXXXXX
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| Code mirror: https://github.com/xiannanhuang/FORESEE
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| Unzip FORESEE_replication_package.zip. All relative paths below are relative to the FORESEE/ root.
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| The archive contains:
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| 1. dataset/<dataset_name>/train.npy, val.npy, test.npy, adj_mx.npy for nycbike, nyctaxi, chibike, chitaxi, bosbike, baybike, torbike.
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| 2. saved_models/ checkpoints that reproduce the paper tables (14 files):
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| STGCN and GWNET on all seven datasets.
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| 3. models/*_config.yaml and model source files.
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| 4. Source codes: train.py, foresee.py, eval_reproduce.py, fintune_opencity.py, Dataset.py, modules under models/ and tools/, and OpenCity configs in opencity/*.yaml.
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| 5. expected_results/paper_metrics.csv for checking reproduced MAE/RMSE.
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| Data description
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| We provide processed secondary demand tensors, not raw trip records. These files are sufficient to reproduce the verified Ori and FORESEE numbers listed above.
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| Each dataset folder contains:
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| - train.npy / val.npy / test.npy: hourly inflow/outflow demand. Accepted shapes: (nodes, days, 24, 2) or hour-first (hours, nodes, 2). The last dimension is 2 (pick-up and drop-off, or equivalent inflow/outflow).
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| - adj_mx.npy: spatial adjacency among zones, float32, shape (N, N).
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| The tensors are aggregated from publicly released bike-sharing and taxi trip records of New York City, Chicago, Boston, the San Francisco Bay Area, and Toronto. We split them chronologically into train/validation/test and drop zones whose training-period mean demand is <= 2 (paper Section 5.1.1). After this filter, valid zone counts are: nycbike 74, nyctaxi 125, chibike 48, chitaxi 28, bosbike 43, baybike 36, torbike 36.
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| Raw trip-level microdata are not redistributed because they are large and already public from the original operators or open-data portals. The processed tensors fully determine the reported metrics.
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| Code description
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| Required environment:
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| - OS: Windows or Linux
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| - Python >= 3.8
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| - GPU recommended (device: cuda:0 in YAML); CPU works if device is set to cpu
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| - packages listed in requirements.txt: torch, numpy, scikit-learn, pyyaml, scipy, pandas, tqdm, statsmodels
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| Install:
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| pip install -r requirements.txt
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| Main reproduction (no retraining):
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| python foresee.py
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| This evaluates the 14 verified checkpoint pairs and writes online.csv. Compare with expected_results/paper_metrics.csv.
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| Subset evaluation:
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| python eval_reproduce.py --jobs stgcn --out logs/reproduce_stgcn.jsonl --device cuda:0
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| python eval_reproduce.py --jobs gwnet --out logs/reproduce_gwnet.jsonl --device cuda:0
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| FORESEE online settings (foresee.py, class Online_day):
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| - residual experts with EMA coefficients [0.7, 0.8, 0.9, 1.0]
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| - expert-weight temperature eta=10
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| - adaptive spatiotemporal smoothing: graph diffusion (alpha init 0.01) and temporal kernel [0.2, 0.6, 0.2], updated by SGD (lr=0.01)
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| - test loader batch size = 24 (one day); the residual is updated once per day using yesterday's error
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| - look-back window = 6 hours; prediction horizon = 1 hour
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| OpenCity backbone (optional)
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| The main STGCN / GWNET tables do not require OpenCity. To reproduce the OpenCity experiments we provide fintune_opencity.py and the dataset configs under opencity/*.yaml. The official OpenCity pretrained weights are not redistributed here; download OpenCity-base.pth from the OpenCity authors and place it at opencity/OpenCity-base.pth.
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| Step 1. Fine-tune OpenCity on a target dataset. In fintune_opencity.py, set
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| config_path = r'opencity/<dataset>.yaml'
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| and uncomment
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| torch.save(model.state_dict(), 'finetuned_opencity_<dataset>.pth')
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| Default example is chibike. Then run:
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| python fintune_opencity.py
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| Step 2. Run FORESEE on the fine-tuned OpenCity model. In foresee.py, switch init_model to
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| llm='opencity'
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| config_path = r'opencity/<dataset>.yaml'
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| and load the matching checkpoint, e.g.
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| torch.load(r'finetuned_opencity_<dataset>.pth')
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| When constructing Online_day and calling online(), also pass llm='opencity' so that the OpenCity forward signature is used. Then run:
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| python foresee.py
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| Change <dataset> among nycbike, nyctaxi, chibike, chitaxi, bosbike, baybike, torbike. Keep device in the corresponding yaml consistent with the local GPU.
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| Simulation software description
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| Not applicable. This study does not use traffic microsimulation. All experiments are supervised forecasting on historical demand tensors.
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| Experiment design description
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| Not applicable. There are no surveys, questionnaires, or human-subject experiments. Evaluation is rolling hourly prediction on the held-out test split, reported as MAE and RMSE after inverse standardization.
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| Others
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| 1. Zone filter and adjacency indexing must stay consistent: paper_valid_grid() selects zones and load_paper_adj() slices adj_mx with the same index.
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| 2. Features are standardized with a scaler fitted only on training data (CustomStandardScaler); MAE/RMSE are computed after inverse_transform.
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| 3. Set device in each models/*_config.yaml to match the local machine.
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| 4. All 14 STGCN / GWNET checkpoints are released.
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