Urban Human Systems Intelligence · CITY-INTEL-01 · Version 1.0.0 · May 2026

CITYMIND

Urban Human Systems Intelligence Framework
Independent Subsystem Modeling with AI-Enhanced Aggregation
Transport Flow · Population Density · Energy Consumption · Mobility Behavior · Infrastructure Load

"A city is not a single unified system — it is a collection of independent subsystems whose interaction must be governed
through aggregation, not structural merging. CITYMIND treats each urban subsystem as analytically separate, applying AI
only as a bounded optimization layer for the final composite indicator: the Urban Health Index (UHI)."

↗ View on GitHub 📦 PyPI Package 🔬 Zenodo DOI 🦊 GitLab Mirror

Real-Time Urban Governance

Four-Level UHI Signal Classification

Every urban configuration evaluated by CITYMIND receives a continuous safety signal with full subsystem diagnostics.

UHI ≥ 0.85

🟢 OPTIMIZED URBAN FLOW

All subsystems within design bounds. Normal operation active — continuous monitoring.

0.70 ≤ UHI < 0.85

🟠 STRESSED SUBSYSTEM WARNING

One subsystem under elevated stress (transport or energy). Enhanced monitoring — targeted anomaly detection.

0.55 ≤ UHI < 0.70

🟠 SYSTEMIC MITIGATION PHASE

Multiple subsystem efficiency decline. Immediate load balancing — route rerouting, demand management.

UHI < 0.55

🔴 CRITICAL INFRASTRUCTURE BREACH

Safety threshold breached. Immediate operational shutdown — site evacuation — emergency assessment.

Governance Architecture

Five Subsystems · One Composite Index · Continuous Assessment

Each subsystem remains structurally independent and analytically separate. AI provides weight optimization and anomaly detection only.

SUBSYSTEM 01

TFS — Transport Flow Subsystem

T_score = [1 - (V_demand/C_capacity)] + ε_T

BPR congestion function with AI correction (ε_T ≤ 0.05). Real-time traffic volume measurement.

T(v) = T₀·[1 + α·(V/C)^β]

SUBSYSTEM 02

PDS — Population Density Subsystem

P_score = 1 - |(Pop/Area) - D_opt|/D_max

Spatial distribution analysis. Optimal density tracking. Live census data fusion.

Δ = |D_current - D_optimal|/D_max

SUBSYSTEM 03

ECS — Energy Consumption Subsystem

E_score = (Supply_grid - Demand_urban)/Supply_grid

Supply-demand balance. Peak load monitoring. Smart meter data integration.

S-D margin ≥ 0.15

SUBSYSTEM 04

MBS — Mobility Behavior Subsystem

M_score = (Trips_public + Trips_active)/Total_trips

Mode share analysis. Sustainable mobility tracking. Multinomial logit model.

P(choice) = exp(V)/Σexp(V)

SUBSYSTEM 05

ILS — Infrastructure Load Subsystem

I_score = 1 - (Load_infra / Load_critical)

Water, power, waste, transport, telecom, housing composite. K=6 systems.

L_geo = (Πᵢ load_i)^(1/6)

AI ENHANCEMENT

AISL — Adaptive Weight Calibration

Σwᵢ = 1.0 · wᵢ ≥ 0

Gradient boosting weight predictor. Mahalanobis anomaly detection. 48h UHI forecast.

UHI = w_T·T + w_P·P + w_E·E + w_M·M + w_I·I

Experimental Validation

Three Canonical City Scenarios

Validated against megacity, industrial city, and smart city testbeds under congestion, energy, and mobility scenarios.

Case	City Type / Scenario	UHI Accuracy	TFS Error	ECS Error	Anomaly Detection	Status
V1	Megacity — peak congestion + grid stress	±4.1%	±3.7%	±3.8%	92.3%	✅ PASS
V2	Industrial city — energy deficit scenario	±3.9%	±4.1%	±3.4%	90.8%	✅ PASS
V3	Smart city testbed — modal shift optimization	±4.6%	±3.4%	±3.6%	91.7%	✅ PASS
MEAN	—	±4.2%	±3.73%	±3.6%	91.6%	🏆 CERTIFIED

UHI certification threshold = 0.85 · Subsystem independence verified · AI bounded to optimization layer only

Mathematical Foundation

Governing Equations & Safety Bounds

UHI ≥ 0.85

Urban Health Index

T_score ≥ 0.70

Transport threshold

E_score ≥ 0.60

Energy margin

I_score ≥ 0.70

Infrastructure load

Quick Start

Deploy Urban Intelligence in 60 Seconds

pip install citymind-engine

from citymind import CityMindAssessor

# Initialize assessor
assessor = CityMindAssessor()

# Run full CITYMIND pipeline
result = assessor.evaluate()

print(result.uhi_result.uhi)          # Urban Health Index ∈ [0,1]
print(result.uhi_result.signal.value)  # OPTIMIZED_FLOW | STRESSED_WARNING | SYSTEMIC_MITIGATION | CRITICAL_BREACH
print(result.subsystem_scores)        # {T,P,E,M,I} scores
print(result.ai_weights)              # AI-optimized weights Σ=1.0
print(result.anomaly_detected)        # Mahalanobis distance > 3σ

from citymind.subsystems import TransportSubsystem

transport = TransportSubsystem(capacity=10000)

# Simulate traffic demand
score = transport.compute(demand=7500)
level = transport.get_congestion_level(7500)

print(f"T_score: {score:.3f}")
print(f"Status: {level}")

from citymind.subsystems import EnergySubsystem

energy = EnergySubsystem(supply_grid=1000)

# Simulate energy demand
score = energy.compute(demand=600)
status = energy.get_energy_status(600)

print(f"E_score: {score:.3f}")
print(f"Status: {status}")

# Launch real-time Streamlit UHI governance dashboard
$ streamlit run examples/streamlit_dashboard.py

# Dashboard at: http://localhost:8501
# Panels: UHI gauge · Subsystem scores · AI weights · 48h forecast

# Or open web dashboard:
# https://citymind-v1.netlify.app/dashboard

Citation

Cite CITYMIND in Your Research

If CITYMIND contributes to your research, please use one of the citation formats below.

@software{baladi2026citymind_pypi,
  author    = {Baladi, Samir},
  title     = {{CITYMIND}: Urban Human Systems Intelligence Framework —
               Independent Subsystem Modeling with AI-Enhanced Aggregation},
  year      = {2026},
  version   = {1.0.0},
  publisher = {Python Package Index},
  url       = {https://pypi.org/project/citymind-engine},
  note      = {Python package, MIT License, Series CITY-INTEL-01}
}

@dataset{baladi2026citymind_zenodo,
  author    = {Baladi, Samir},
  title     = {{CITYMIND}: Urban Human Systems Intelligence Framework —
               Research Paper and Simulation Data},
  year      = {2026},
  publisher = {Zenodo},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.20444647},
  url       = {https://doi.org/10.5281/zenodo.20444647},
  note      = {Urban Intelligence · CITY-INTEL-01}
}

@misc{baladi2026citymind_osf,
  author    = {Baladi, Samir},
  title     = {{CITYMIND} Framework: Pre-registered Study Protocol for
               Urban Human Systems Intelligence},
  year      = {2026},
  publisher = {Open Science Framework},
  doi       = {10.17605/OSF.IO/AN2HV},
  url       = {https://doi.org/10.17605/OSF.IO/AN2HV},
  note      = {OSF Preregistration}
}

Baladi, S. (2026). CITYMIND: Urban Human Systems Intelligence Framework
(Version 1.0.0, Series CITY-INTEL-01). Zenodo.
https://doi.org/10.5281/zenodo.20444647