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Synthetic Smart City IoT Dataset — Polluted High-Traffic Scenario, 672 Rows

Smart City & Urban Sensing Smart City

Jul 28, 2026

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672 rows · 64 columns

IoTSyn v4.4.0

License

Abstract

"Free CC0 synthetic dataset: 672 rows of UTCI thermal comfort, PM2.5 and NO2 air quality, occupancy and LAeq noise. Polluted High-Traffic Scenario."

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.

672 data points, 64 columns. Config: IoTSyn v4.4.0 engine; master_seed: 88; Capacity: 500; Controls: pollution=high, traffic=extreme; EXTENDED (non-normative).

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

Machine-readable Sidecars

Normalized scenario + controls (reproducibility): scenario.json
Domain validation report (recomputed invariants & coverage): validation.json
Elevated-risk episodes: episodes.json
Behaviour-change strategy file (priority-ranked, provenance-tagged): strategy.json

Sample Data (18 rows · 64 columns)

#	timestamp	step_duration_seconds	day_type	cloud_state	cloud_fraction	precipitation_mm_h	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	0.32	0	24.95	1,729.79	54.91	0.89	0.33	22.90	normal	stochastic	6	6	15	0.03	4	5	1	1	2	2	families	468.85	208.38	24	23.78	23.23	24.50	true	390.71	40	39.66	36.41	39.92	true	58.07	true	24.34	no thermal stress	false	low	caution	caution	low	caution	air_quality	["air_quality","noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue air_quality.
2	2026-07-14T00:15:00+01:00	900	weekday	partly_cloudy	0.41	0	24.37	1,633.18	53.67	0.87	0.32	22.59	normal	stochastic	10	10	16	0.03	9	3	1	2	1	0	general_public	479.91	213.29	24	23.78	23.72	24.51	true	399.92	40	39.66	47.44	40.04	true	57.23	true	23.72	no thermal stress	false	low	caution	caution	low	caution	air_quality	["air_quality","noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue air_quality.
3	2026-07-14T00:30:00+01:00	900	weekday	partly_cloudy	0.59	2.74	23.61	1,734.66	59.65	1.25	0.46	22.39	normal	stochastic	2	2	9	0.02	4	2	0	2	1	0	general_public	467.85	207.93	24	23.83	23.63	24.52	true	389.88	40	39.74	43.93	40.00	true	58.27	true	23.11	no thermal stress	false	low	caution	caution	low	caution	air_quality	["air_quality","noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue air_quality.
4	2026-07-14T00:45:00+01:00	900	weekday	partly_cloudy	0.74	1.69	22.58	1,723.75	63.11	0.57	0.21	21.80	normal	stochastic	4	4	8	0.02	2	2	0	2	2	0		526.81	234.14	24	23.73	26.63	24.58	true	439.01	40	39.60	34.59	39.99	true	59.89	true	22.86	no thermal stress	false	low	caution	caution	low	caution	air_quality	["air_quality","noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue air_quality.
5	2026-07-14T01:00:00+01:00	900	weekday	partly_cloudy	0.48	0	22.52	1,671.66	61.42	0.94	0.35	20.95	busy	stochastic	21	21	27	0.05	8	9	1	6	2	1	families	551.94	245.31	24	23.81	24.33	24.56	true	459.95	40	39.73	43.05	40.02	true	58.80	true	22.19	no thermal stress	false	low	caution	caution	low	caution	air_quality	["air_quality","noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue air_quality.
6	2026-07-14T01:15:00+01:00	900	weekday	overcast	0.68	0	22.75	1,529.83	55.43	0.32	0.12	21.80	busy	stochastic	10	10	28	0.06	10	8	2	6	1	1	general_public	548.53	243.79	24	23.64	23.40	24.51	true	457.10	40	39.48	35.68	39.99	true	57.48	true	22.55	no thermal stress	true	low	caution	caution	low	caution	air_quality	["air_quality","noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue air_quality.
7	2026-07-14T01:30:00+01:00	900	weekday	overcast	0.78	0	22.72	1,549.70	56.24	0.29	0.11	22.05	busy	stochastic	12	12	23	0.05	9	5	2	5	2	0	general_public	618.96	275.09	24	23.66	26.12	24.55	true	515.80	40	39.53	33.92	39.91	true	60.80	true	22.66	no thermal stress	true	low	caution	caution	low	caution	air_quality	["air_quality","noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue air_quality.
8	2026-07-14T01:45:00+01:00	900	weekday	partly_cloudy	0.59	0	22.42	1,577.77	58.31	0.30	0.11	21.19	busy	stochastic	14	14	26	0.05	14	6	0	3	2	1	general_public	758.47	337.10	24	23.77	21.79	24.46	true	632.06	40	39.72	41.12	39.94	true	58.68	true	22.30	no thermal stress	true	low	caution	caution	low	caution	air_quality	["air_quality","noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue air_quality.
9	2026-07-14T02:00:00+01:00	900	weekday	partly_cloudy	0.38	0	21.60	1,546.79	60.11	0.49	0.18	19.73	busy	stochastic	9	9	21	0.04	8	3	2	3	2	3	general_public	701.57	311.81	24	23.79	22.61	24.43	true	584.64	40	39.73	44.08	40.00	true	60.57	true	21.35	no thermal stress	true	low	caution	caution	low	caution	air_quality	["air_quality","noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue air_quality.
10	2026-07-14T02:15:00+01:00	900	weekday	partly_cloudy	0.46	0	20.84	1,544.86	62.90	0.35	0.13	19.22	normal	stochastic	13	13	23	0.05	6	7	3	3	2	2	families	665.52	295.79	24	23.73	23.84	24.42	true	554.60	40	39.64	37.98	40.02	true	60.40	true	20.80	no thermal stress	true	low	caution	caution	low	caution	air_quality	["air_quality","noise"]	[]	false	1	false	action_caution	Overall action level caution; dominant issue air_quality.

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.

Scenario + controls Normalized scenario configuration and control parameters — everything needed to reproduce the run scenario.json Validation report Recomputed invariants, coverage checks and Kolmogorov–Smirnov results for this dataset validation.json Risk episodes Table of elevated-risk episodes detected across the simulated period episodes.json Strategy file Priority-ranked, provenance-tagged behaviour-change strategy objects strategy.json

Cite This Dataset

IoTSyn Generated (2026). Synthetic Smart City IoT Dataset — Polluted High-Traffic Scenario, 672 Rows. [Dataset]. IoTSyn Generated. https://iotsyn.com/view.php?uid=iotsyn_6a641d61390725.82951930

Source: IoTSyn Generated (2026)

Indexed by IoTDataset.com on Jul 28, 2026

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Synthetic Smart City IoT Dataset — Polluted High-Traffic Scenario, 672 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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Share This Research

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