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pushthetempo
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astana_traffic_geospatial_v1

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Tabular Classification

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train

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:    CastError
Message:      Couldn't cast
partitionScheme: string
prefix: string
description: string
parts: struct<2026-06-16T21: struct<path: string, rowCount: int64, hourKey: timestamp[s], updatedAt: string (... 2 chars omitted)
  child 0, 2026-06-16T21: struct<path: string, rowCount: int64, hourKey: timestamp[s], updatedAt: string>
      child 0, path: string
      child 1, rowCount: int64
      child 2, hourKey: timestamp[s]
      child 3, updatedAt: string
totalRows: int64
updatedAt: string
derivedFrom: string
streamKind: string
stage: string
vehicle_type: string
traffic_density: double
Severity: string
severity: string
speedKmh: int64
latitude: double
speed_kmh: int64
activeModelId: string
sourceMode: string
city: string
sourceType: string
transport: string
event_type: string
businessResult: string
sensorKind: string
Event_Type: string
sensorType: string
sourceTimestamp: string
location: string
sourceName: string
eventType: string
longitude: double
Traffic_Density: double
vehicleType: string
timestamp: string
sourceId: string
eventId: string
confidence: double
Longitude: double
eventTime: string
Speed_kmh: int64
Latitude: double
trafficDensity: double
to
{'stage': Value('string'), 'eventId': Value('string'), 'eventTime': Value('string'), 'sourceId': Value('string'), 'sourceName': Value('string'), 'sourceType': Value('string'), 'sourceMode': Value('string'), 'sensorType': Value('string'), 'location': Value('string'), 'activeModelId': Value('string'), 'city': Value('string'), 'Latitude': Value('float64'), 'Severity': Value('string'), 'latitude': Value('float64'), 'severity': Value('string'), 'speedKmh': Value('int64'), 'Longitude': Value('float64'), 'Speed_kmh': Value('int64'), 'eventType': Value('string'), 'longitude': Value('float64'), 'speed_kmh': Value('int64'), 'timestamp': Value('string'), 'transport': Value('string'), 'Event_Type': Value('string'), 'confidence': Value('float64'), 'event_type': Value('string'), 'sensorKind': Value('string'), 'streamKind': Value('string'), 'derivedFrom': Value('string'), 'vehicleType': Value('string'), 'vehicle_type': Value('string'), 'trafficDensity': Value('float64'), 'Traffic_Density': Value('float64'), 'sourceTimestamp': Value('string'), 'traffic_density': Value('float64'), 'businessResult': Value('string')}
because column names don't match
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 478, 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 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, 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/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              partitionScheme: string
              prefix: string
              description: string
              parts: struct<2026-06-16T21: struct<path: string, rowCount: int64, hourKey: timestamp[s], updatedAt: string (... 2 chars omitted)
                child 0, 2026-06-16T21: struct<path: string, rowCount: int64, hourKey: timestamp[s], updatedAt: string>
                    child 0, path: string
                    child 1, rowCount: int64
                    child 2, hourKey: timestamp[s]
                    child 3, updatedAt: string
              totalRows: int64
              updatedAt: string
              derivedFrom: string
              streamKind: string
              stage: string
              vehicle_type: string
              traffic_density: double
              Severity: string
              severity: string
              speedKmh: int64
              latitude: double
              speed_kmh: int64
              activeModelId: string
              sourceMode: string
              city: string
              sourceType: string
              transport: string
              event_type: string
              businessResult: string
              sensorKind: string
              Event_Type: string
              sensorType: string
              sourceTimestamp: string
              location: string
              sourceName: string
              eventType: string
              longitude: double
              Traffic_Density: double
              vehicleType: string
              timestamp: string
              sourceId: string
              eventId: string
              confidence: double
              Longitude: double
              eventTime: string
              Speed_kmh: int64
              Latitude: double
              trafficDensity: double
              to
              {'stage': Value('string'), 'eventId': Value('string'), 'eventTime': Value('string'), 'sourceId': Value('string'), 'sourceName': Value('string'), 'sourceType': Value('string'), 'sourceMode': Value('string'), 'sensorType': Value('string'), 'location': Value('string'), 'activeModelId': Value('string'), 'city': Value('string'), 'Latitude': Value('float64'), 'Severity': Value('string'), 'latitude': Value('float64'), 'severity': Value('string'), 'speedKmh': Value('int64'), 'Longitude': Value('float64'), 'Speed_kmh': Value('int64'), 'eventType': Value('string'), 'longitude': Value('float64'), 'speed_kmh': Value('int64'), 'timestamp': Value('string'), 'transport': Value('string'), 'Event_Type': Value('string'), 'confidence': Value('float64'), 'event_type': Value('string'), 'sensorKind': Value('string'), 'streamKind': Value('string'), 'derivedFrom': Value('string'), 'vehicleType': Value('string'), 'vehicle_type': Value('string'), 'trafficDensity': Value('float64'), 'Traffic_Density': Value('float64'), 'sourceTimestamp': Value('string'), 'traffic_density': Value('float64'), 'businessResult': Value('string')}
              because column names don't match

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Flowmatic Smart City Dataset

Flowmatic smart-city dataset with hourly UTC CSV partitions. 1 hour file(s), 820 total row(s). Files live under data/hourly/ and are indexed in data/hourly/manifest.json.

Pipeline run: cmqh5oog206mto42sdzqeve6w
Updated: 2026-06-16T21:30:29.003Z
Partition scheme: hourly-utc
Manifest: data/hourly/manifest.json

Hourly CSV layout

Rows are grouped by eventTime into one CSV per UTC hour:

Directory: data/hourly/
File pattern: YYYY-MM-DDTHH.csv (example: data/hourly/2026-05-21T14.csv)
Append exports merge only within the touched hour file(s), not the whole dataset.

Hour (UTC)	File	Rows	Updated
2026-06-16 21:00–21:59 UTC	`data/hourly/2026-06-16T21.csv`	820	2026-06-16T21:30:29.003Z

Preview (latest exported rows)

stage	eventId	eventTime	sourceId	sourceName	sourceType	sourceMode	sensorType	location	activeModelId	city	Latitude	Severity	latitude	severity	speedKmh	Longitude	Speed_kmh	eventType	longitude	speed_kmh	timestamp	transport	Event_Type	confidence	event_type	sensorKind	streamKind	derivedFrom	vehicleType	vehicle_type	trafficDensity	Traffic_Density	sourceTimestamp	traffic_density	businessResult
"business"	"AST-003271"	"2026-06-16T21:30:24.915Z"	"cmqh4kcix00ndo42s4a8wq9so"	"Astana Traffic Geospatial"	"WEBSOCKET"	"SIMULATED"	"traffic"	"Astana"	"production:q1_astana_tranad_anomaly_detector"	"Astana"	51.242688	"Medium"	51.242688	"Medium"	64	71.53689	64	"Normal"	71.53689	64	"2026-06-16T21:30:24.915Z"	"WEBSOCKET"	"Normal"	0.953	"Normal"	"traffic"	"astana_semi_synthetic"	"astana_synthetic_data.csv"	"Motorcycle"	"Motorcycle"	199	199	"2024-08-11T04:18:38.551Z"	199	"{"activeModelId":"production:q1_astana_tranad_anomaly_detector","skipped":true,"reason":"Select a Hugging Face model in the core unit configuration"}"
"business"	"AST-003269"	"2026-06-16T21:30:23.915Z"	"cmqh4kcix00ndo42s4a8wq9so"	"Astana Traffic Geospatial"	"WEBSOCKET"	"SIMULATED"	"traffic"	"Astana"	"production:q1_astana_tranad_anomaly_detector"	"Astana"	51.13708	"Critical"	51.13708	"Critical"	37	71.369984	37	"Accident"	71.369984	37	"2026-06-16T21:30:23.914Z"	"WEBSOCKET"	"Accident"	0.905	"Accident"	"traffic"	"astana_semi_synthetic"	"astana_synthetic_data.csv"	"Taxi"	"Taxi"	79	79	"2024-09-29T12:15:18.482Z"	79	"{"activeModelId":"production:q1_astana_tranad_anomaly_detector","skipped":true,"reason":"Select a Hugging Face model in the core unit configuration"}"
"business"	"AST-003267"	"2026-06-16T21:30:22.911Z"	"cmqh4kcix00ndo42s4a8wq9so"	"Astana Traffic Geospatial"	"WEBSOCKET"	"SIMULATED"	"traffic"	"Astana"	"production:q1_astana_tranad_anomaly_detector"	"Astana"	51.182072	"High"	51.182072	"High"	46	71.390786	46	"Accident"	71.390786	46	"2026-06-16T21:30:22.911Z"	"WEBSOCKET"	"Accident"	0.88	"Accident"	"traffic"	"astana_semi_synthetic"	"astana_synthetic_data.csv"	"Bus"	"Bus"	58.56	58.56	"2024-06-22T23:23:46.272Z"	58.56	"{"activeModelId":"production:q1_astana_tranad_anomaly_detector","skipped":true,"reason":"Select a Hugging Face model in the core unit configuration"}"
"business"	"AST-003265"	"2026-06-16T21:30:21.908Z"	"cmqh4kcix00ndo42s4a8wq9so"	"Astana Traffic Geospatial"	"WEBSOCKET"	"SIMULATED"	"traffic"	"Astana"	"production:q1_astana_tranad_anomaly_detector"	"Astana"	51.22073	"Critical"	51.22073	"Critical"	8	71.418081	8	"Congestion"	71.418081	8	"2026-06-16T21:30:21.908Z"	"WEBSOCKET"	"Congestion"	0.892	"Congestion"	"traffic"	"astana_semi_synthetic"	"astana_synthetic_data.csv"	"Taxi"	"Taxi"	54.2	54.2	"2024-09-03T19:29:01.264Z"	54.2	"{"activeModelId":"production:q1_astana_tranad_anomaly_detector","skipped":true,"reason":"Select a Hugging Face model in the core unit configuration"}"
"business"	"AST-003263"	"2026-06-16T21:30:20.907Z"	"cmqh4kcix00ndo42s4a8wq9so"	"Astana Traffic Geospatial"	"WEBSOCKET"	"SIMULATED"	"traffic"	"Astana"	"production:q1_astana_tranad_anomaly_detector"	"Astana"	51.208814	"Critical"	51.208814	"Critical"	80	71.425837	80	"Accident"	71.425837	80	"2026-06-16T21:30:20.907Z"	"WEBSOCKET"	"Accident"	0.917	"Accident"	"traffic"	"astana_semi_synthetic"	"astana_synthetic_data.csv"	"Car"	"Car"	95.11	95.11	"2024-07-10T01:52:58.629Z"	95.11	"{"activeModelId":"production:q1_astana_tranad_anomaly_detector","skipped":true,"reason":"Select a Hugging Face model in the core unit configuration"}"

Usage

from datasets import load_dataset

# Loads all hourly CSV parts as one split
ds = load_dataset("pushthetempo/astana_traffic_geospatial_v1", data_files="data/hourly/*.csv", split="train")

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Total file size:

494 kB