Transportation
17 columns
200 rows
8 formats

Urban Intersection Traffic Timeseries

This dataset provides detailed, hourly traffic volume metrics for urban intersections, including vehicle, pedestrian, bicycle, and public transit counts, enriched with weather and incident context. It is ideal for congestion analysis, infrastructure planning, and predictive modeling, empowering city planners, mobility startups, and researchers to optimize urban mobility and forecast traffic trends.

last updated 22 Oct 2025
by GoMask

The brief that made it

Congestion prediction and trend analysis for city planners

Sample rows

preview · 8 of 200 rows · all 17 columns

intersection_idstring	congestion_levelstring	pedestrian_countinteger	incident_reportedboolean	statestring	intersection_namestring	latitudefloat	longitudefloat	citystring	countrystring	measurement_datedate	measurement_hourinteger	traffic_volumeinteger	bicycle_countinteger	public_transit_countinteger	weather_conditionstring	temperature_celsiusfloat
NYC-US-001	high	632	true	NY	5th Ave & 42nd St	40.7538	-73.9845	New York	USA	2024-06-03	8	1203	43	27	cloudy	21.7
TOR-CA-002	medium	192	false	ON	King St W & Spadina Ave	43.6442	-79.3958	Toronto	Canada	2024-06-03	9	523	22	16	rainy	17.3
BER-DE-003	low	22	false	BE	Alexanderplatz & Karl-Liebknecht-Str	52.5219	13.4131	Berlin	Germany	2024-06-03	21	67	5	2	clear	10.5
DEL-IN-004	severe	781	true	DL	Connaught Place & Janpath	28.6315	77.2177	Delhi	India	2024-06-04	18	1319	28	33	windy	35.6
TKY-JP-005	severe	892	true	blank	Shibuya Crossing	35.6595	139.7004	Tokyo	Japan	2024-06-04	19	1452	61	34	clear	30.2
SYD-AU-006	medium	118	false	NSW	George St & Bathurst St	-33.8748	151.2077	Sydney	Australia	2024-06-04	14	384	14	9	cloudy	18.2
JOH-ZA-007	high	312	false	GT	Oxford Rd & Rosebank	-26.1467	28.0373	Johannesburg	South Africa	2024-06-05	17	876	7	19	clear	21.1
RIO-BR-008	high	544	true	RJ	Avenida Atlântica & Rua Figueiredo Magalhães	-22.9676	-43.1802	Rio de Janeiro	Brazil	2024-06-05	8	1042	11	17	clear	28.7
PAR-FR-009	high	382	false	IDF	Boulevard Haussmann & Rue du Havre	48.8745	2.3277	Paris	France	2024-06-05	7	912	32	24	foggy	14.3
OSL-NO-010	low	3	false	03	Storgata & Hausmanns Gate	59.917	10.7557	Oslo	Norway	2024-06-06	23	25	0	1	foggy	7.2
HEL-FI-011	low	1	false	blank	Mannerheimintie & Kaivokatu	60.1708	24.9402	Helsinki	Finland	2024-06-06	5	11	0	0	snowy	-2.7
MUM-IN-012	high	501	true	MH	Marine Drive & Churchgate	18.9438	72.8258	Mumbai	India	2024-06-07	17	1128	9	25	rainy	26.3
LAG-NG-013	medium	233	false	blank	Lekki-Epe Expressway & Admiralty Way	6.4318	3.4839	Lagos	Nigeria	2024-06-07	15	783	4	11	clear	33.6
MEX-MX-014	high	448	true	CMX	Avenida Insurgentes & Paseo de la Reforma	19.4315	-99.1512	Mexico City	Mexico	2024-06-08	18	1190	13	23	windy	22.4
CHI-US-015	medium	312	false	IL	State St & Madison St	41.8822	-87.6278	Chicago	USA	2024-06-08	7	845	26	15	cloudy	16.1
STO-SE-016	medium	102	false	blank	Sergels Torg & Hamngatan	59.3326	18.0649	Stockholm	Sweden	2024-06-09	12	305	12	10	clear	14.8
LON-GB-017	medium	221	false	blank	Oxford Circus & Regent St	51.5154	-0.1419	London	UK	2024-06-09	13	428	19	12	cloudy	16.9
SYD-AU-018	high	319	false	NSW	Market St & Pitt St	-33.8707	151.2071	Sydney	Australia	2024-06-09	17	792	17	20	windy	16.2
LOS-US-019	high	658	true	CA	Sunset Blvd & Vine St	34.0983	-118.3267	Los Angeles	USA	2024-06-10	18	1245	28	31	clear	27.1
MEL-AU-020	medium	122	false	VIC	Swanston St & Collins St	-37.8173	144.9671	Melbourne	Australia	2024-06-10	11	384	14	10	cloudy	13.3

What the 200 rows show

from the 200-row sample

Severe (congestion level) stands out: mean pedestrian_count is 760.4, against 239.0 for the rest.

28%incident_reported = true
229.5median pedestrian_count
7weather conditions
35countries
45cities
13median measurement_hour

Mean pedestrian_count by congestion_level200 rows

pedestrian_count200 rows, in bands of 100

Median 229.5, from 0 to 892.

state118 rows with a value · 82 left blank

NY15
NSW11
IDF11
MH11
CA10
ON9
GT9
BE6
DL6
RJ6

17 columns by typefrom the column list below

string 7
integer 5
float 3
date 1
boolean 1

Columns

17 columns in four groups

blueprint · 17 columns
column	type	description	example
Text 7 columns
`intersection_id`	string	Unique identifier for the urban intersection where traffic is measured	NYC-US-001
`intersection_name`	string	Human-readable name or description of the intersection	5th Ave & 42nd St
`city`	string	City where the intersection is located	New York
`state`	string	State or region where the intersection is locatedoptional	NY
`country`	string	Country where the intersection is located	USA
`weather_condition`	string	General weather condition during measurement (e.g., clear, cloudy, rainy, snowy)7 values · optional	cloudy
`congestion_level`	string	Categorical congestion level at the intersection during the specified hourlow · medium · high · severe · optional	high
Numbers 8 columns
`latitude`	float	Latitude coordinate of the intersection-90 to 90	40.7538
`longitude`	float	Longitude coordinate of the intersection-180 to 180	-73.9845
`measurement_hour`	integer	Hour of the day when traffic was measured (0-23)0 to 23	8
`traffic_volume`	integer	Number of vehicles passing through the intersection during the specified hour0 or more	1203
`pedestrian_count`	integer	Number of pedestrians crossing the intersection during the specified hour0 or more · optional	632
`bicycle_count`	integer	Number of bicycles passing through the intersection during the specified hour0 or more · optional	43
`public_transit_count`	integer	Number of public transit vehicles (buses, trams, etc.) passing through the intersection during the specified hour0 or more · optional	27
`temperature_celsius`	float	Ambient temperature at the intersection (in degrees Celsius) during measurement-50 to 60 · optional	21.7
Dates and times 1 column
`measurement_date`	date	Date of traffic measurement (YYYY-MM-DD)	2024-06-03
True or false 1 column
`incident_reported`	boolean	Indicates if any traffic incident (accident, roadwork, etc.) was reported during the measurement houroptional	true

Use it for

A transportation dashboard
The incident_reported rate, pedestrian_count by congestion_level and a breakdown of state. Excel, Power BI or Tableau.
Why do the 17 severe rows have a mean pedestrian_count of 760.4?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Intersections200
NYC-US-001632high
TOR-CA-002192medium
DEL-IN-004781severe
A software demo
Believable intersections with intersection_name, latitude and longitude to fill a screen in front of a buyer.

This dataset200 rows17 columns

Yours1,000 rows17 columnscity: UK only

blueprint · urban-intersection-traffic-timeseries

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 17 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 per day
Traffic volume measured hourly between 6AM-10PM
Randomized intersection IDs ensure privacy; no real identifiers
Includes weather and event flags impacting traffic
Synthetic time-series patterns mimic real-world rush hours
Missing data points simulated for sensor outages

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

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: urban-intersection-traffic-timeseries

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A transportation dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Transportation

What should your data show?