Type 0
Traditional
City systems are largely isolated and manually operated.
- Limited instrumentation
- Limited real-time connectivity
- Mostly manual operations
- Data stored in separate departmental systems
Quantitative methodology v0.2.3 · Developed by Dr. Khurshudov
Smart City Evaluation & Classification Methodology
How SCD Measures Smart City Technology and Autonomy
Type Score measures demonstrated technological maturity and breadth; Autonomy Score measures operational decision authority. Smart City Dispatch’s original evidence-based method assesses documented deployments across eight equally weighted service domains. The scores are independent: a city may demonstrate advanced technology while people retain operating authority. Published rules, eligibility gates, coverage treatment, and source evidence make assessments reproducible.
See the international Smart City Atlas and its Paris city assessment for examples of the method applied to published evidence.
Active quantitative methodology v0.2.3
The Eight Scoring Domains and Their Weights
| Code | Domain | Weight |
|---|---|---|
| MOB | Mobility and transportation | 0.125 — 12.5% |
| ENG | Energy and smart grids | 0.125 — 12.5% |
| WAT | Water and wastewater | 0.125 — 12.5% |
| BLD | Buildings and infrastructure | 0.125 — 12.5% |
| SAF | Public safety and emergency response | 0.125 — 12.5% |
| ENV | Environment and waste management | 0.125 — 12.5% |
| GOV | Digital government and public services | 0.125 — 12.5% |
| HLT | Healthcare and social services | 0.125 — 12.5% |
| TOTAL | Eight service domains | 1.000 — 100% |
The eight service domains receive equal weight: wd = 0.125. Education maps into GOV for scoring. The 21 capability domains describe evidence; they are not additional scoring buckets. The 68 technology patterns are reference definitions, not evidence of city deployment.
How the Two Scores Are Calculated
Smart City Type Score
Accessible text equation
S_T = 100 times the sum across eight domains of domain weight times the maximum eligible deployment contribution: eligibility gate times coverage coefficient times technology maturity divided by 3.
S_T = 100 × Σ(d=1…8) [w_d × max(i∈d)(g_i × c_i × T_i / 3)]Smart City Autonomy Score
Accessible text equation
S_A = 100 times the sum across eight domains of domain weight times the maximum eligible deployment contribution: eligibility gate times coverage coefficient times operational autonomy divided by 3.
S_A = 100 × Σ(d=1…8) [w_d × max(i∈d)(g_i × c_i × A_i / 3)]| Symbol | Meaning |
|---|---|
| ST | Smart City Type Score, 0–100 |
| SA | Smart City Autonomy Score, 0–100 |
| d | One of eight service domains |
| i | A documented technology deployment within the domain |
| wd | Domain weight, 0.125 |
| Ti | Demonstrated technology maturity, 0–3 |
| Ai | Demonstrated operational autonomy, 0–3 |
| ci | Geographic/service coverage coefficient |
| gi | Eligibility gate, 0 or 1 |
| max | Highest eligible contribution within the domain |
- Each domain contributes at most one winning Type value and one winning Autonomy value. The winners can be different technologies.
- Multiple technologies within one domain are not added together.
- Insufficient evidence does not automatically receive credit. Missing evidence does not establish that a technology is absent.
Coverage is assessed for each axis. Where the demonstrated maturity and autonomy have different operating scopes, the implementation retains separate cT and cA coefficients. Sum underlying unrounded domain contributions, then round the final score half-up to two decimal places.
Technology Maturity and Autonomy Assessment
These are independent axes. Unknown T or A remains explicitly unknown when evidence is insufficient. Type 3 technological maturity does not establish citywide Level D autonomy.
| Value | Demonstrated capability |
|---|---|
| T0 | Traditional/manual or insufficient demonstrated connected intelligence |
| T1 | Connected sensing, information collection, and observation |
| T2 | Intelligence, prediction, analytics, and operational optimization support |
| T3 | Advanced adaptive or goal-directed technological intelligence, subject to operating evidence requirements |
| Value | Demonstrated operational authority |
|---|---|
| A0 | No qualifying delegated higher-tier operational decision authority demonstrated under the assessed scope |
| A1 | Actionable AI/data-driven advice or decision support; humans retain operational authority |
| A2 | Context-dependent operational action selection and execution with measured feedback and delegated authority |
| A3 | Goal-directed autonomous optimization, operational tradeoffs, adaptation/reoptimization, and governance controls |
Deployment Coverage and Eligibility
| Coverage | Code | Coefficient |
|---|---|---|
| Isolated site or limited project | P | 0.15 |
| District or neighborhood | D | 0.35 |
| Substantial operating urban service network | N | 0.65 |
| Broadly deployed relevant urban service | W | 1.00 |
The coefficient represents a service-coverage classification, not a literal percentage of city land area or population. Unknown coverage is unknown — not scored; no numeric coefficient is established for an unknown category.
g = 1 requires a verified, currently operating, materially city-serving deployment at N/W coverage that satisfies all other evidence requirements. g = 0 — excluded from the whole-city quantitative score normally applies to pilot, district-only, planned, discontinued or insufficiently verified deployments.
A substantial transit network, urban service fleet or utility may qualify when it serves the city, even if it crosses municipal boundaries or does not reach every neighborhood. Document the meaningful in-city operating scope. An isolated installation must not elevate the entire municipality.
Smart City Type and Autonomy Thresholds
| Type | Minimum Type Score | Additional requirement |
|---|---|---|
| Type 1 | 5 | At least 2 distinct eligible domains with T ≥ 1 |
| Type 2 | 10 | At least 2 distinct eligible domains with T ≥ 2 |
| Type 3 | 30 | At least 3 distinct eligible domains with T = 3 and required corroboration |
| Level | Minimum Autonomy Score | Additional requirement |
|---|---|---|
| B | 2.5 | At least 1 eligible domain with A ≥ 1 |
| C | 10 | At least 2 distinct eligible domains with verified A ≥ 2 contextual closed-loop action |
| D | 30 | At least 3 distinct eligible domains with verified A = 3 goal-directed optimization |
Worked Example — Moscow
2BType Score 37.29Autonomy Score 17.92Published v0.2.3 assessment
| Domain | Type contribution | Autonomy contribution |
|---|---|---|
| MOB | 8.12500000 | 5.41666667 |
| ENG | 8.33333333 | 4.16666667 |
| WAT | 4.16666667 | 0.00000000 |
| BLD | 0.00000000 | 0.00000000 |
| SAF | 8.33333333 | 4.16666667 |
| ENV | 0.00000000 | 0.00000000 |
| GOV | 0.00000000 | 0.00000000 |
| HLT | 8.33333333 | 4.16666667 |
Calculations use unrounded values. The displayed domain contributions are rounded for readability. Final reported totals: Type Score = 37.29; Autonomy Score = 17.92. For example, MOB Type contributes 100 × 0.125 × 0.65 × 3/3 = 8.125 points.
- Type 2 qualifies: the score exceeds 10, and four eligible domains demonstrate T ≥ 2.
- Type 3 does not qualify: only one eligible domain demonstrates T3; three are required, even though the score exceeds 30.
- Level B qualifies: the score exceeds 2.5, and four eligible domains demonstrate A ≥ 1.
- Level C does not qualify: only one eligible domain demonstrates A2; two are required, even though the score exceeds 10.
Moscow Decision Tables → · Supporting evidence → · Research report →
Smart City Types 0–3
Type describes demonstrated maturity, from traditional operations to advanced adaptive intelligence.
Type 1
Connected / Observe
City systems increasingly collect and share real-time information.
- Sensors and IoT devices
- Connected infrastructure
- Centralized monitoring
- Dashboards and alerts
- Improved situational awareness
Type 2
Intelligent / Predict and Optimize
Data and analytics help identify patterns, predict conditions, and improve operations.
- Predictive analytics
- AI-assisted decision support
- Digital twins
- Optimization and forecasting
- Cross-system data integration
Type 3
Autonomous / Decide and Adapt
Selected urban systems demonstrate advanced adaptive or goal-directed technological intelligence under operating evidence requirements.
- Automated decision systems
- Adaptive infrastructure
- Autonomous mobility or operations
- Real-time optimization and closed-loop control
- Continuous learning and adaptation
Type 3 does not mean the entire city operates autonomously; it is distinct from Level D decision authority.
Smart City Autonomy Levels A–D
Level A
Human Controlled
People monitor conditions and make operational decisions. Technology primarily provides information.
Level B
AI Assisted
AI and analytics recommend actions, while people make or approve the final decision.
Level C
Contextual Automated Execution
Qualifying systems independently select and execute operational actions based on changing real-world conditions, measure the outcomes, and use feedback to inform subsequent decisions. Human oversight, operational boundaries, and intervention authority remain in place.
Level D
Autonomous Optimization
Systems perform goal-directed autonomous optimization, manage operational tradeoffs, and adapt or reoptimize using measured feedback. People set objectives, policies, safety constraints, intervention authority, and governance.
Conventional fixed-rule operation alone does not qualify for Level C
Fixed traffic-light schedules, simple threshold-triggered actions, conventional PID/PLC regulation, automatic meter readings and preprogrammed transaction workflows do not by themselves demonstrate contextual operational autonomy.
Level C requires substantive operating evidence of sensing, alternative selection, delegated action, execution, measured feedback and appropriate service coverage. Level D additionally requires goal-directed optimization and reoptimization.
Evidence Quality, Limitations, and Unknown Classifications
Failed retrieval, no verified technology found, or a zero documented Autonomy Score does not establish Type 0 or Level A. Those classifications require affirmative evidence. Otherwise, preserve an unresolved or insufficient-evidence designation; autonomy can remain ?, as in 1?.
Distinguish original sources examined, historical evidence carried forward, search-excerpt-only evidence, inaccessible originals, verified operating deployments, best-evidence inferences needing further verification, and unverified or excluded technology. An excerpt-only inference is not independently verified original-source evidence. Repeated links to one publication do not provide independent corroboration.
Operating and planned capabilities are separate. Planned-design benchmarks, including NEOM 3D*, have null operating scores. Each edition preserves sources, examination states, eligibility and coverage judgments, decision tables and historical reports. Cities can have uneven capabilities across service systems.
The 21 Descriptive Capability Domains and 68 Technology Patterns
This taxonomy organizes research; it adds no score buckets and creates no automatic deployment credit. Digital twins receive no automatic score bonus.
Expand the 21 descriptive capability domains
- Mobility and transportation
- Energy and smart grid
- Water and utilities
- Buildings
- Public safety and resilience
- Environment and monitoring
- Digital government and citizen services
- Sensing and instrumentation
- Connectivity and data infrastructure
- Cross-system integration
- Real-time operations
- Analytics and AI
- Prediction and forecasting
- Optimization
- Digital twins
- Autonomous systems, robotics, and drones
- Automated control
- Autonomous decision-making
- Human oversight and governance
- Manual and fragmented urban systems
- Deployment scale
The 68 patterns are conditional reference definitions. Every city deployment still needs evidence of its demonstrated capability, operating status, coverage and decision authority.
Detailed Calculations and Further Tools
Approved quantitative editions use the v0.2.3 engine. Legacy Research City and visitor tools have their own input/publication workflows; their outputs do not automatically replace an authoritative published research edition.