Hourly Traffic Flow Time Series
This dataset provides granular, hourly traffic flow measurements at urban intersections, including vehicle counts, flow rates, direction, and contextual factors like weather and incidents. Designed for mobility analytics, congestion prediction, and machine learning, it enables urban planners and data scientists to optimize real-time traffic management and model urban mobility patterns.
Sample rows
preview · 8 of 202 rows · all 13 columns| record_idstring | sensor_typestring | vehicle_countinteger | incident_flagboolean | intersection_idstring | intersection_namestring | latitudefloat | longitudefloat | timestampdatetime | flow_ratefloat | directionstring | lane_countinteger | weather_conditionstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TX-001-northbound-2024-06-01T08:00:00 | camera | 412 | false | TX-001 | Houston Main @ | 29.7604 | -95.3698 | 2024-06-01T08:00:00 | 6.87 | northbound | 5 | clear |
| CA-002-eastbound-2024-06-01T09:00:00 | camera | 368 | false | CA-002 | San Francisco Market St | 37.7749 | -122.4194 | 2024-06-01T09:00:00 | 6.14 | eastbound | 4 | fog |
| NY-001-southbound-2024-06-01T10:00:00 | camera | 520 | false | NY-001 | Manhattan 5th & 42nd | 40.7537 | -73.9846 | 2024-06-01T10:00:00 | 8.66 | southbound | 6 | clear |
| WA-001-westbound-2024-06-01T11:00:00 | radar | 310 | false | WA-001 | Seattle Pine & 3rd | 47.6101 | -122.3421 | 2024-06-01T11:00:00 | 5.12 | westbound | 4 | rain |
| IL-001-northbound-2024-06-01T12:00:00 | camera | 600 | false | IL-001 | Chicago Lake & Michigan | 41.8925 | -87.6141 | 2024-06-01T12:00:00 | 10 | northbound | 7 | clear |
| FL-001-southbound-2024-06-01T13:00:00 | loop_detector | 200 | false | FL-001 | Miami Biscayne & 8th | 25.7886 | -80.2264 | 2024-06-01T13:00:00 | 3.36 | southbound | 3 | rain |
| NV-001-eastbound-2024-06-01T14:00:00 | camera | 397 | false | NV-001 | Las Vegas Strip @ | 36.1024 | -115.1742 | 2024-06-01T14:00:00 | 6.62 | eastbound | 5 | clear |
| NM-001-westbound-2024-06-01T15:00:00 | radar | 82 | false | NM-001 | Albuquerque Central & 2nd | 35.0844 | -106.6504 | 2024-06-01T15:00:00 | 1.37 | westbound | 2 | clear |
| CA-001-northbound-2024-06-01T16:00:00 | camera | 485 | false | CA-001 | Los Angeles Sunset Blvd | 34.0983 | -118.3258 | 2024-06-01T16:00:00 | 8.09 | northbound | 6 | clear |
| TX-002-other-2024-06-01T17:00:00 | loop_detector | 44 | false | TX-002 | Dallas Elm & Main | 32.7825 | -96.7947 | 2024-06-01T17:00:00 | 0.73 | other | 2 | other |
| WA-002-northbound-2024-06-01T18:00:00 | radar | 94 | false | WA-002 | Tacoma Pacific & 21st | 47.2529 | -122.4443 | 2024-06-01T18:00:00 | 1.56 | northbound | 3 | rain |
| INT-EDGE-90-180-other-2024-06-01T02:00:00 | other | 1 | false | INT-EDGE-90-180 | Edge Arctic Intersection | 90 | 180 | 2024-06-01T02:00:00 | 0.02 | other | 1 | snow |
| IL-002-eastbound-2024-06-01T20:00:00 | loop_detector | 27 | false | IL-002 | Springfield Oak & 7th | 39.799 | -89.6437 | 2024-06-01T20:00:00 | 0.45 | eastbound | 2 | fog |
| FL-002-westbound-2024-06-01T21:00:00 | radar | 13 | false | FL-002 | Orlando Colonial & Mills | 28.5467 | -81.3679 | 2024-06-01T21:00:00 | 0.24 | westbound | 2 | rain |
| NM-002-southbound-2024-06-01T22:00:00 | other | 6 | false | NM-002 | Santa Fe Agua Fria & St. | 35.687 | -105.9378 | 2024-06-01T22:00:00 | 0.09 | southbound | 1 | other |
| INT-EDGE-90-180-northbound-2024-06-01T00:00:00 | other | 0 | true | INT-EDGE-90-180 | Edge Arctic Intersection | 90 | 180 | 2024-06-01T00:00:00 | 0 | northbound | 1 | snow |
| NY-002-westbound-2024-06-01T03:00:00 | radar | 4 | false | NY-002 | Brooklyn Atlantic & Flatbush | 40.6836 | -73.9758 | 2024-06-01T03:00:00 | 0.07 | westbound | 2 | clear |
| CA-002-southbound-2024-06-01T04:00:00 | radar | 7 | false | CA-002 | San Francisco Market St | 37.7749 | -122.4194 | 2024-06-01T04:00:00 | 0.13 | southbound | 2 | fog |
| NV-002-other-2024-06-01T05:00:00 | other | 2 | false | NV-002 | Reno Virginia & 4th | 39.5296 | -119.8138 | 2024-06-01T05:00:00 | 0.03 | other | 1 | other |
| TX-001-eastbound-2024-06-01T06:00:00 | loop_detector | 15 | false | TX-001 | Houston Main @ | 29.7604 | -95.3698 | 2024-06-01T06:00:00 | 0.26 | eastbound | 5 | clear |
What the 202 rows show
from the 202-row sampleCamera (sensor type) stands out: mean vehicle_
- 15%incident_
flag = true - 75median vehicle_
count - 5directions
- 5weather conditions
- 59intersection names
- 1.3median flow_
rate
Median 75, from 0 to 600.
- string 6
- integer 2
- float 3
- datetime 1
- boolean 1
Columns
13 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 6 columns | |||
record_id | string | Unique identifier for each hourly traffic flow recordunique | TX-001-northbound-2024-06… |
intersection_id | string | Unique identifier for the intersection where traffic is measured | TX-001 |
intersection_name | string | Human-readable name or description of the intersectionoptional | Houston Main @ |
direction | string | Direction of traffic flow measured (e.g., northbound, southbound, eastbound, westbound)northbound · southbound · eastbound · westbound · other | northbound |
sensor_type | string | Type of sensor used for measurement (e.g., loop detector, camera, radar)loop_detector · camera · radar · other · optional | camera |
weather_condition | string | Weather condition during measurement (e.g., clear, rain, snow, fog)clear · rain · snow · fog · other · optional | clear |
| Numbers 5 columns | |||
latitude | float | Latitude coordinate of the intersection-90 to 90 | 29.7604 |
longitude | float | Longitude coordinate of the intersection-180 to 180 | -95.3698 |
vehicle_count | integer | Total number of vehicles detected during the hour0 or more | 412 |
flow_rate | float | Average vehicle flow rate (vehicles per minute) during the hour0 or more | 6.87 |
lane_count | integer | Number of lanes monitored for this measurement1 or more · optional | 5 |
| Dates and times 1 column | |||
timestamp | datetime | Date and hour for the traffic measurement (start of hour) | 2024-06-01T08:00:00 |
| True or false 1 column | |||
incident_flag | boolean | Indicates if a traffic incident (accident, roadwork, etc.) was reported during this houroptional | false |
Use it for
A transportation dashboard
The incident_
flag rate, vehicle_ count by sensor_ type and a breakdown of intersection_ id. Excel, Power BI or Tableau. Why do the 70 camera rows have a mean vehicle_
count of 339.3? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Records202TX-001-northbound-2024-06-01T08:00:00412cameraINT-EDGE-90-180-northbound-2024-06-01T00:00:000otherNV-002-northbound-2024-06-01T23:00:000other
A software demo
Believable records with intersection_
id, intersection_ name and latitude to fill a screen in front of a buyer.
This dataset202 rows13 columns
Yours1,000 rows13 columns
blueprint · hourly-traffic-flow-time-series
Behind this dataset
Same schema. As many rows as you need.
These 202 rows came out of a blueprint — 13 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Each row represents one intersection at one hour
- Vehicle counts must be positive integers
- Include weather impact indicator for each row
- Add geolocation coordinates for each intersection
- Peak and off-peak hours flagged for each entry
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- hourly-traffic-flow-time-series