Dataset Overview
| Dataset Name | Smart City Solution Dataset |
| Total Data Volume | 15,200 (programme file + technical parameters + implementation case + bid information) |
| Coverage | Smart traffic, smart environment, smart city management, smart community, digital government, urban brain, smart emergency. |
| Time Span | 2018 - 2026 |
| Data Source | Government Procurement Network, tender platforms, corporate open programmes, pilot summary report on smart cities |
| Annotation Method | Industry analyst label + technical expert cross-check |
| Format Output | JSON / CSV / Markdown / PDF Summary |
| Update Frequency | Monthly increment update |
Detailed Description of Data Dimensions
| Field Name | Data Type | Value Range / Description |
|---|---|---|
| solution_id | String(32) | Program_UNIQUE_TECH_Evidence_UNIQUE_TECH__... |
| domain | Enum | SMART_TRAFFIC | SMART_ENV | SMART_CITY_MGMT | DIGITAL_GOV | CITY_BRAIN | SMART_EMERGENCY |
| city_tier | Enum | TIER1 | TIER2 | TIER3 | COUNTY |
| budget_range | Enum | < 5 million ⁇ 5 million - 20 million ⁇ 20-50 million ⁇ 50-100 million ⁇ & gt; 100 million |
| tech_stack | Array | IoT | 5G | AI | BigData | Cloud | Edge | Blockchain | DigitalTwin |
| key_vendors | Array | List of participating manufacturers (de-identified) |
| implementation_stage | Enum | PLANNING | BIDDING | DEPLOYING | OPERATING | COMPLETED |
| kpi_metrics | Object | {metric_name: {target_value, actual_value, unit}} |
| data_sources | Array | [{source_type, volume, frequency, format}] |
| roi_analysis | Object | {investment, annual_saving, payback_period_months} |
De-identified JSON Data Sample
[ { "solution_id": "SMC-2024-GD-SZ-0301", "domain": "SMART_TRAFFIC", "city_tier": "TIER1", "budget_range": ">1", "tech_stack": ["IoT", "5G", "AI", "BigData", "Edge"], "key_vendors": ["", "", ""], "implementation_stage": "OPERATING", "kpi_metrics": { "avg_congestion_reduction": {"target_value": 15, "actual_value": 18.2, "unit": "%"}, "emergency_response_time": {"target_value": 8, "actual_value": 6.5, "unit": "min"} }, "data_sources": [ {"source_type": "Camera", "volume": 12000, "frequency": "daily", "format": ""}, {"source_type": "Sensors", "volume": 500000, "frequency": "realtime", "format": ""} ], "roi_analysis": {"investment": 28000, "annual_saving": 5200, "payback_period_months": 65} }, { "solution_id": "SMC-2023-ZJ-HZ-0147", "domain": "CITY_BRAIN", "city_tier": "TIER1", "budget_range": "5000-1", "tech_stack": ["Cloud", "AI", "BigData", "DigitalTwin"], "key_vendors": ["", ""], "implementation_stage": "DEPLOYING", "kpi_metrics": { "gov_service_online_rate": {"target_value": 95, "actual_value": 88.5, "unit": "%"}, "data_sharing_coverage": {"target_value": 90, "actual_value": 76.3, "unit": "%"} }, "data_sources": [ {"source_type": "Public Livelihooddata", "volume": 2000000, "frequency": "daily", "format": "Structured"} ], "roi_analysis": {"investment": 8500, "annual_saving": 1200, "payback_period_months": 85} }, { "solution_id": "SMC-2024-HN-CS-0082", "domain": "SMART_ENV", "city_tier": "TIER2", "budget_range": "2000-5000", "tech_stack": ["IoT", "AI", "Cloud"], "key_vendors": ["Smart City.", ""], "implementation_stage": "OPERATING", "kpi_metrics": { "air_quality_monitoring": {"target_value": 98, "actual_value": 99.2, "unit": "%"}, "pollution_source_traceability": {"target_value": 85, "actual_value": 82.7, "unit": "%"} }, "data_sources": [ {"source_type": "", "volume": 365000, "frequency": "hourly", "format": ""}, {"source_type": "", "volume": 52, "frequency": "weekly", "format": ""} ], "roi_analysis": {"investment": 3200, "annual_saving": 480, "payback_period_months": 80} }, { "solution_id": "SMC-2025-SC-CD-0023", "domain": "SMART_CITY_MGMT", "city_tier": "TIER2", "budget_range": "500-2000", "tech_stack": ["IoT", "AI", "Cloud"], "key_vendors": ["", ""], "implementation_stage": "BIDDING", "kpi_metrics": { "incident_response_time": {"target_value": 30, "actual_value": null, "unit": "min"}, "citizen_satisfaction": {"target_value": 90, "actual_value": null, "unit": "%"} }, "data_sources": [ {"source_type": "", "volume": 8000, "frequency": "daily", "format": ""} ], "roi_analysis": {"investment": 1500, "annual_saving": 300, "payback_period_months": 60} }, { "solution_id": "SMC-2022-JS-NJ-0198", "domain": "DIGITAL_GOV", "city_tier": "TIER1", "budget_range": "2000-5000", "tech_stack": ["Cloud", "AI", "Blockchain"], "key_vendors": ["", ""], "implementation_stage": "COMPLETED", "kpi_metrics": { "one_stop_service_rate": {"target_value": 90, "actual_value": 94.3, "unit": "%"}, "document_processing_time": {"target_value": 3, "actual_value": 1.8, "unit": "day"} }, "data_sources": [ {"source_type": "Public LivelihoodServices", "volume": 15000000, "frequency": "daily", "format": "Structured"} ], "roi_analysis": {"investment": 4200, "annual_saving": 980, "payback_period_months": 51} }
]
AI Training Use Cases
Generate smart urban planning programmes
Applying model: LLM (GPT-4o/Claude) + RAG. Automatically generate customized smart urban planning programmes based on historical and urban characteristics.
Urban run-off forecast
Applying model: TFT / Autoformer / PatchTST. Training time series prediction models based on multi-dimensional urban run data for traffic, environment, energy consumption, etc.
Smart City ROI Assessment
Applying model: XGBoost + SHAP. Project ROI for Projecting and Evaluating Smart Cities based on historical project input output data.
Digital twin city modelling
Applying model: NeRF / 3D Gaussia Spratting + Cesium. Trained three-dimensional urban landscape reconstruction and visualization models based on city multi-source data.
Get This Dataset
The Smart Cities Programme data sets support the customization of mandates by area and city level.
137-5502-0164 Online Consultation