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UniTE-chengdu

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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ArrowInvalid
Message:      Mismatching child array lengths
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 83, in _generate_tables
                  pa_table = _recursive_load_arrays(h5, self.info.features, start, end)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 267, in _recursive_load_arrays
                  arr = _recursive_load_arrays(obj, features[path], start, end)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 288, in _recursive_load_arrays
                  sarr = pa.StructArray.from_arrays(values, names=keys)
                File "pyarrow/array.pxi", line 4304, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Mismatching child array lengths

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UniTE Chengdu Dataset

Chengdu taxi trajectory dataset for the UniTE paper: UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings.

Paper: arXiv | IEEE

Overview

This dataset contains real-world taxi trajectories collected in Chengdu, China. Each HDF5 file stores trajectory sequences along with trip metadata and road network information, formatted for use with the UniTE pipeline.

File Structure 

UniTE_chengdu/
├── chengdu.h5              # Full dataset (~1.2 GB)
├── small_chengdu.h5        # Small sample for debugging (~8.8 MB)
└── split/
    ├── chengdu_train.h5    # Training split
    ├── chengdu_valid.h5    # Validation split
    └── chengdu_test.h5     # Test split

HDF5 File Format

Each .h5 file contains one dataset with three keys (DataFrames):

  • trips: Trajectory sequences (point-level GPS records)
  • trip_info: Additional features per trajectory (user ID, class label, etc.)
  • road_info: Road network information (coordinates of each road segment)

Data Statistics

File Size Description
chengdu.h5 ~1.2 GB Full Chengdu taxi trajectory dataset
small_chengdu.h5 ~8.8 MB Small sample subset
split/chengdu_train.h5 ~926 MB Training split
split/chengdu_valid.h5 ~119 MB Validation split
split/chengdu_test.h5 ~116 MB Test split

Usage with UniTE 

# Preprocess the dataset
export META_PATH=../cache
export DATASET_PATH=UniTE_chengdu
cd src
singularity run --nv ../unite.sif data.py --name chengdu -t trip

Or load directly with pandas:

import pandas as pd
data = pd.read_hdf("chengdu.h5", key="trips")

References

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Paper for Davidham3/UniTE-chengdu