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Synthetic Smart City IoT Dataset — 700 Rows

Smart City & Urban Sensing Smart City

Jul 28, 2026

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700 rows · 64 columns

IoTSyn v4.4.0

License

Abstract

"Free CC0 synthetic dataset: 700 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.

700 data points, 64 columns. Config: IoTSyn v4.4.0 engine; master_seed: 123456; Capacity: 500.

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	clear	0.10	26.31	1,914.09	56.04	3.01	1.10	23.63	normal	stochastic	10	10	34	0.07	13	9	7	3	0	2	general_public	212.48	47.22	12	11.94	11.97	12.05	true	88.53	20	19.91	20.31	20.00	true	57.22	true	23.63	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	clear	0.19	26.85	1,915.23	54.34	1.64	0.60	24.41	normal	stochastic	8	8	24	0.05	10	5	4	4	0	1	general_public	175.52	39	12	11.90	11.73	12.02	true	73.13	20	19.83	17.73	19.99	true	55.56	true	25.78	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	clear	0.26	26.42	1,941.31	56.49	2.85	1.04	24.20	quiet	stochastic	6	6	19	0.04	8	3	2	5	0	1	general_public	175.30	38.95	12	11.94	12.82	12.03	true	73.04	20	19.90	21.25	20.01	true	56.38	true	24.11	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	clear	0.15	26.03	1,823.31	54.27	4.79	1.76	23.48	quiet	stochastic	8	8	18	0.04	6	3	2	6	0	1		117.74	26.16	12	11.96	13.55	12.03	true	49.06	20	19.93	20.24	20.00	true	56.17	true	21.26	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	clear	0.16	25.43	1,817.59	56.05	2.93	1.07	22.92	quiet	stochastic	0	0	11	0.02	5	1	2	3	0	0	general_public	94.15	20.92	12	11.93	9.78	12.01	true	39.23	20	19.89	20.58	19.99	true	55.45	true	22.64	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	clear	0.42	25.32	1,714.01	53.21	1.20	0.44	23.58	busy	stochastic	20	20	27	0.05	11	2	7	3	1	3	general_public	90.80	20.18	12	11.86	12.98	12.02	true	37.83	20	19.76	20.33	20.00	true	57.26	true	24.51	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	clear	0.24	25.02	1,695.19	53.59	1.42	0.52	22.74	busy	stochastic	18	18	36	0.07	10	8	7	2	4	5	general_public	86.01	19.11	12	11.87	12.81	12.02	true	35.84	20	19.79	19.50	19.97	true	56.95	true	23.84	no thermal stress	false	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	clear	0.14	24.89	1,595.86	50.84	0.69	0.25	22.30	busy	stochastic	19	19	44	0.09	16	7	9	3	4	5	general_public	86.08	19.13	12	11.78	11.06	12.01	true	35.87	20	19.64	23.32	20.02	true	57.54	true	23.95	no thermal stress	false	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	0.17	24.36	1,614.54	53.07	0.59	0.22	21.87	normal	stochastic	3	3	28	0.06	11	5	5	3	3	1	general_public	91.89	20.42	12	11.76	11.90	12.02	true	38.29	20	19.60	22.02	20.06	true	56.96	true	23.60	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	clear	0.28	24.09	1,581.64	52.84	1.26	0.46	21.93	normal	stochastic	14	14	31	0.06	9	5	6	5	3	3	general_public	106.06	23.57	12	11.86	10.90	12.02	true	44.19	20	19.77	16.81	20.06	true	59.16	true	22.93	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 — 700 Rows. [Dataset]. IoTSyn Generated. https://iotsyn.com/view.php?uid=iotsyn_6a4e3d05b2ec13.61781155

Source: IoTSyn Generated (2026)

Indexed by IoTDataset.com on Jul 28, 2026

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Synthetic Smart City IoT Dataset — 700 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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