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Synthetic Smart City IoT Dataset — 300 Rows

Smart City & Urban Sensing Smart City

Jul 28, 2026

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300 rows · 64 columns

IoTSyn v4.4.0

License

Abstract

"Free CC0 synthetic dataset: 300 rows of UTCI thermal comfort, PM2.5 and NO2 air quality, occupancy and LAeq noise."

Description

Overview

Synthetic urban public-space IoT time series generated by the IoTSyn v4.4.0 reference engine (Smart City & Public Spaces Edition). A coupled physical–stochastic model produces meteorology (solar geometry, cloud/precipitation Markov chains, AR(1) air temperature with a nocturnal urban-heat-island term, and a Weibull wind field induced through a Gaussian copula), human presence (a state-modulated non-homogeneous Poisson arrival process with LogNormal dwell times and infinite-server occupancy), road traffic, species-resolved air quality from a well-mixed mass-balance box model (PM2.5 and NO2, with separate physical and sensor columns), environmental noise composed in the energy domain (LAeq), and UTCI thermal comfort. A three-way separation between physical truth, sensor observation, and decision output is preserved throughout, and a multi-hazard decision layer emits an ordinal urban risk vector (thermal, air quality, noise, crowding), operational action levels, risk episodes, and provenance-tagged behaviour-change strategy objects. All randomness derives from named substreams of a portable MT19937 generator; identical configuration and seed reproduce byte-identical output. Each dataset ships with machine-readable sidecars: normalized scenario, domain validation report, risk episodes, and the strategy file.

300 data points, 64 columns. Config: IoTSyn v4.4.0 engine; master_seed: 123456; Capacity: 3400.

Column Schema

Column	Description
`timestamp`	string_iso8601 — interval start, explicit UTC offset
`step_duration_seconds`	integer, s
`day_type`	enum {weekday \| weekend \| holiday}
`cloud_state`	enum {clear \| partly_cloudy \| overcast}
`cloud_fraction`	float, dimensionless — range 0..1
`precipitation_mm_h`	float, mm/h — >=0; exactly 0 when dry
`air_temperature_c`	float, degC
`vapour_pressure_pa`	float, Pa
`relative_humidity_pct`	float, % — 0..100 after roundoff clamp
`wind_speed_10m_m_s`	float, m/s
`wind_speed_pedestrian_m_s`	float, m/s
`mean_radiant_temperature_c`	float, degC
`crowd_state`	enum {quiet \| normal \| busy \| event}
`crowd_state_mechanism`	enum {stochastic \| forced}
`offered_arrivals_count`	integer, persons
`admitted_arrivals_count`	integer, persons
`rejected_arrivals_count`	integer, persons
`deferred_arrivals_count`	integer, persons
`deferred_timeout_count`	integer, persons
`queue_length_persons`	integer, persons — 0 unless capacity_mode=defer_fifo
`occupancy_persons`	integer, persons
`operational_capacity_ratio`	float, dimensionless
`audience_general_public_persons`	integer, persons
`audience_families_persons`	integer, persons
`audience_elderly_persons`	integer, persons
`audience_students_persons`	integer, persons
`audience_commuters_persons`	integer, persons
`audience_event_attendees_persons`	integer, persons
`dominant_audience`	enum, nullable {general_public \| families \| elderly \| students \| commuters \| event_attendees}
`traffic_flow_vehicles_h`	float, vehicles/h
`pm25_emission_ug_s`	float, ug/s
`pm25_background_ug_m3`	float, ug/m3
`pm25_physical_ug_m3`	float, ug/m3
`pm25_sensor_raw_ug_m3`	float, ug/m3
`pm25_sensor_calibrated_ug_m3`	float, ug/m3, nullable
`pm25_rolling_24h_ug_m3`	float, ug/m3, nullable — null until complete window
`pm25_rolling_complete`	boolean
`no2_emission_ug_s`	float, ug/s
`no2_background_ug_m3`	float, ug/m3
`no2_physical_ug_m3`	float, ug/m3
`no2_sensor_raw_ug_m3`	float, ug/m3
`no2_sensor_calibrated_ug_m3`	float, ug/m3, nullable
`no2_rolling_24h_ug_m3`	float, ug/m3, nullable — null until complete window
`no2_rolling_complete`	boolean
`LAeq_15min_dBA`	float, dB(A), nullable — column name embeds the declared window; null until complete window unless warm-up supplies history
`noise_window_complete`	boolean
`utci_c`	float, degC
`utci_stress_category`	enum {extreme cold stress \| very strong cold stress \| strong cold stress \| moderate cold stress \| slight cold stress \| no thermal stress \| moderate heat stress \| strong heat stress \| very strong heat stress \| extreme heat stress}
`utci_input_clamped`	boolean
`wbgt_outdoor_c`	float, degC, nullable
`thermal_risk_level`	enum, nullable {low \| caution \| high_risk \| severe}
`air_quality_risk_level`	enum, nullable {low \| caution \| high_risk \| severe}
`noise_risk_level`	enum, nullable {low \| caution \| high_risk \| severe}
`crowding_risk_level`	enum, nullable {low \| caution \| high_risk \| severe}
`overall_action_level`	enum, nullable {low \| caution \| high_risk \| severe}
`dominant_issue`	enum, nullable {thermal \| air_quality \| noise \| crowding}
`co_dominant_issues`	json_array_of_enum {thermal \| air_quality \| noise \| crowding}
`elevated_issues`	json_array_of_enum {thermal \| air_quality \| noise \| crowding}
`elevated_hazard_count`	integer
`compound_risk_flag`	boolean
`risk_completeness_fraction`	float, dimensionless
`overall_action_level_provisional`	boolean
`risk_communication_template_id`	string
`risk_communication_message`	string

Mathematical Models

Meteorology: AR(1) air temperature + sinusoidal diurnal cycle + nocturnal urban-heat-island (Oke); Magnus–WMO saturation vapour pressure; Weibull 10 m wind via an AR(1) Gaussian copula (Sklar), pedestrian wind through a log profile
Thermal comfort: UTCI operational polynomial (Bröde et al. 2012) from air temperature, mean radiant temperature, 10 m wind and vapour pressure; clamp-and-flag domain policy; 10 scientific categories plus a coarser 4-level operational band
Air quality: Single well-mixed box mass balance with ventilation exchange, deposition and chemical first-order loss and a traffic-driven source; exact exponential one-step update; mean-preserving log-normal sensor observation model; 24 h rolling means
Presence: Discrete-time Markov-modulated NHPP arrivals with thermal/rain/wind deterrence; exact per-interval Poisson counts with sorted uniform offsets; LogNormal dwell; infinite-server occupancy reconstructed from events; largest-remainder audience allocation
Noise: A-weighted equivalent continuous level (LAeq) over a declared window, summed in the energy (intensity) domain per ISO 1996
Risk & strategy: Conservative-max hazard vector with compound-risk indicators; compound priority score H·log10(1 + person-minutes / E_ref); ex-post, provenance-tagged behaviour-change strategies

Use Cases

Thermal-comfort (UTCI) and heat-risk analytics
Air-quality (PM2.5 / NO2) exposure and sensor calibration / drift research
Occupancy and arrivals (NHPP) estimation benchmarks with exact ground truth
Environmental-noise (LAeq) exposure analysis
Multi-hazard urban risk aggregation and decision-support research

Reproducibility

Seed: 123456. Same seed + parameters = identical output. IoTSyn v3.1.

Machine-readable Sidecars

Domain validation report (recomputed invariants & coverage): validation.json

Sample Data (18 rows · 64 columns)

#	timestamp	step_duration_seconds	day_type	cloud_state	cloud_fraction	air_temperature_c	vapour_pressure_pa	relative_humidity_pct	wind_speed_10m_m_s	wind_speed_pedestrian_m_s	mean_radiant_temperature_c	crowd_state	crowd_state_mechanism	offered_arrivals_count	admitted_arrivals_count	occupancy_persons	operational_capacity_ratio	audience_general_public_persons	audience_families_persons	audience_elderly_persons	audience_students_persons	audience_commuters_persons	audience_event_attendees_persons	dominant_audience	traffic_flow_vehicles_h	pm25_emission_ug_s	pm25_background_ug_m3	pm25_physical_ug_m3	pm25_sensor_raw_ug_m3	pm25_rolling_24h_ug_m3	pm25_rolling_complete	no2_emission_ug_s	no2_background_ug_m3	no2_physical_ug_m3	no2_sensor_raw_ug_m3	no2_rolling_24h_ug_m3	no2_rolling_complete	LAeq_15min_dBA	noise_window_complete	utci_c	utci_stress_category	utci_input_clamped	thermal_risk_level	air_quality_risk_level	noise_risk_level	crowding_risk_level	overall_action_level	dominant_issue	co_dominant_issues	elevated_issues	compound_risk_flag	risk_completeness_fraction	overall_action_level_provisional	risk_communication_template_id	risk_communication_message
1	2026-07-14T00:00:00+01:00	900	weekday	overcast	0.89	23.80	1,432.56	48.72	2.97	1.09	23.47	normal	stochastic	4	4	12	0.00	6	2	0	2	1	1	general_public	295.18	65.59	12	11.95	11.33	12.04	true	122.99	20	19.92	20.67	19.84	true	56.66	true	20.69	no thermal stress	false	low	low	caution	low	caution	noise	["noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue noise.
2	2026-07-14T00:15:00+01:00	900	weekday	partly_cloudy	0.34	24.56	1,428.33	46.40	1.07	0.39	22.57	normal	stochastic	4	4	12	0.00	5	1	1	2	2	1	general_public	247.89	55.09	12	11.87	10.98	12.03	true	103.29	20	19.79	19.28	19.84	true	55.96	true	23.27	no thermal stress	false	low	low	caution	low	caution	noise	["noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue noise.
3	2026-07-14T00:30:00+01:00	900	weekday	partly_cloudy	0.49	23.79	1,382.88	47.04	1.83	0.67	22.27	busy	stochastic	15	15	23	0.01	11	4	2	3	2	1	general_public	225.98	50.22	12	11.91	14.03	12.06	true	94.16	20	19.86	18.18	19.83	true	56.96	true	21.82	no thermal stress	false	low	low	caution	low	caution	noise	["noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue noise.
4	2026-07-14T00:45:00+01:00	900	weekday	partly_cloudy	0.60	23.73	1,443.94	49.31	1.30	0.48	22.52	normal	stochastic	9	9	25	0.01	14	6	1	0	2	2	general_public	163.02	36.23	12	11.87	10.89	12.04	true	67.93	20	19.80	17.09	19.85	true	55.61	true	22.57	no thermal stress	false	low	low	caution	low	caution	noise	["noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue noise.
5	2026-07-14T01:00:00+01:00	900	weekday	partly_cloudy	0.82	23.68	1,382.51	47.35	1.45	0.53	23.12	normal	stochastic	19	19	34	0.01	20	8	2	0	3	1	general_public	173.33	38.52	12	11.89	13.64	12.06	true	72.22	20	19.81	17.49	19.83	true	55.37	true	22.44	no thermal stress	false	low	low	caution	low	caution	noise	["noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue noise.
6	2026-07-14T01:15:00+01:00	900	weekday	partly_cloudy	0.16	24.54	1,416.63	46.09	0.94	0.35	22.03	busy	stochastic	14	14	35	0.01	21	6	3	2	1	2	general_public	260.95	57.99	12	11.86	11.75	12.05	true	108.73	20	19.77	21.27	19.82	true	58.19	true	23.16	no thermal stress	false	low	low	caution	low	caution	noise	["noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue noise.
7	2026-07-14T01:30:00+01:00	900	weekday	partly_cloudy	0.65	24.05	1,480.31	49.57	0.27	0.10	23.00	busy	stochastic	17	17	38	0.01	22	8	6	2	0	0	general_public	255.03	56.67	12	11.70	16.19	12.08	true	106.26	20	19.52	22.46	19.86	true	57.28	true	23.51	no thermal stress	true	low	low	caution	low	caution	noise	["noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue noise.
8	2026-07-14T01:45:00+01:00	900	weekday	partly_cloudy	0.54	24.09	1,497.61	50.04	0.46	0.17	22.70	normal	stochastic	15	15	43	0.01	22	8	7	3	2	1	general_public	319.81	71.07	12	11.78	12.66	12.09	true	133.26	20	19.65	18.27	19.85	true	57.49	true	23.48	no thermal stress	true	low	low	caution	low	caution	noise	["noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue noise.
9	2026-07-14T02:00:00+01:00	900	weekday	clear	0.38	23.46	1,429.11	49.59	1.47	0.54	21.61	normal	stochastic	15	15	39	0.01	17	7	5	4	5	1	general_public	407.75	90.61	12	11.92	10.56	12.02	true	169.90	20	19.87	19.14	19.86	true	58.64	true	21.91	no thermal stress	false	low	low	caution	low	caution	noise	["noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue noise.
10	2026-07-14T02:15:00+01:00	900	weekday	partly_cloudy	0.78	23.02	1,435.17	51.16	2.17	0.80	22.36	busy	stochastic	20	20	40	0.01	15	8	4	3	7	3	general_public	285.29	63.40	12	11.93	12.06	12.03	true	118.87	20	19.89	18.58	19.86	true	57.94	true	20.95	no thermal stress	false	low	low	caution	low	caution	noise	["noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue noise.

Showing first 10 of 18 sample rows

Data Visualization Interactive

Numeric Trends

Category Distribution

Machine-Readable Artifacts JSON

This dataset ships companion files alongside the CSV. Together they make the run independently reproducible and auditable without re-running the generator.

Validation report Recomputed invariants, coverage checks and Kolmogorov–Smirnov results for this dataset validation.json

Cite This Dataset

IoTSyn Generated (2026). Synthetic Smart City IoT Dataset — 300 Rows. [Dataset]. IoTSyn Generated. https://iotsyn.com/view.php?uid=iotsyn_6a4e94ceab8d01.08138915

Source: IoTSyn Generated (2026)

Indexed by IoTDataset.com on Jul 28, 2026

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Synthetic Smart City IoT Dataset — 300 Rows

Abstract

Description

Overview

Column Schema

Mathematical Models

Use Cases

Reproducibility

Machine-readable Sidecars

Sample Data (18 rows · 64 columns)

Data Visualization Interactive

Numeric Trends

Category Distribution

Machine-Readable Artifacts JSON

Cite This Dataset

Ready to Start Your Research?

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