A city dispatcher faces tens of thousands of events daily: road accidents, pipe bursts, citizen complaints, weather changes. Most are handled manually, with information scattered across different systems. The result — incident response time exceeds 2 hours, and resources are used inefficiently.
We develop AI platforms for smart cities from scratch. We have launched 12 projects in cities with populations from 300,000 to 2,000,000. Our platform merges data from traffic lights, video surveillance, utility meters, and environmental sensors into a single information field. In real time, the system processes events, detects anomalies, and suggests response scenarios. The platform integrates data from 10+ sources, handles up to 500,000 events per second with latency under 100 ms. City budget savings reach up to 15 million rubles per year by optimizing traffic flows and reducing energy consumption.
The platform is built on microservices with Apache Kafka as the data bus and Apache Flink for stream processing. This setup yields processing latency under 100 ms at peak loads up to 500,000 events per second. Compared to batch processing, this is 600 times faster.
Result — 60% reduction in incident response time and 15% reduction in energy consumption of city infrastructure.
How the AI Platform Unifies Disparate Data?
A modern city generates data from dozens of independent systems. Our platform unifies them through Apache Kafka:
| System | Data | Frequency |
|---|---|---|
| Traffic control (ASUDD) | Traffic flow | Real time |
| Video surveillance (CCTV) | Video streams | Continuous |
| Utilities (ASKUE) | Energy consumption | Every 15 min |
| Environmental monitoring | Air, noise | Every 5–30 min |
| Emergency services (112/EDDS) | Calls, incidents | On event |
| Public transport | GPS, passenger flow | Every 30 sec |
Apache Kafka as data bus + Apache Flink for stream processing:
from confluent_kafka import Consumer, KafkaError
import json
def process_city_events(topics=['transport', 'utilities', 'safety']):
consumer = Consumer({
'bootstrap.servers': 'kafka:9092',
'group.id': 'smart-city-analytics',
'auto.offset.reset': 'latest'
})
consumer.subscribe(topics)
while True:
msg = consumer.poll(timeout=1.0)
if msg is None:
continue
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
continue
break
event = json.loads(msg.value())
topic = msg.topic()
if topic == 'transport':
process_transport_event(event)
elif topic == 'utilities':
process_utility_anomaly(event)
elif topic == 'safety':
process_safety_incident(event)
We use Kafka version 3.5 with retention 7 days, Flink 1.18 with checkpointing every 30 seconds. This guarantees reliable delivery and fault tolerance during failures.
Why Are AI Modules Critical for City Management?
Situation Analysis. The correlation engine links events from different systems: a water main break + resident complaints + traffic due to road closure form a single incident. An NLP classifier processes incoming messages and social media to tie them to known incidents. All events related to an incident are displayed on a timeline.
Predictive Analytics. Multi-domain forecasting predicts the city’s state for 24–72 hours: traffic conditions considering events and weather, peak water/heat/electricity consumption, risk of social conflicts based on public events and social media sentiment.
Smart Transportation
Unified Traffic Management. Centralized control of all transport infrastructure: adaptive traffic light control (not isolated, but system-wide), green wave for public transport, dynamic information boards for drivers.
Parking Analytics. IoT occupancy sensors, ML prediction of parking availability at arrival time considering travel delay, dynamic pricing to redistribute flow.
Environmental Monitoring with AI
Real-time Air Quality Index. Sensor network for PM2.5, PM10, NO₂, O₃, CO → calculate AQI per GOST. Interpolation between stations based on wind and topography data. 12–24 hour air quality forecast. Push notifications for vulnerable groups when AQI worsens.
Urban Heat Islands. Satellite thermal imagery (Landsat Band 10) detects urban heat islands. Correlation with asphalt and green coverage provides recommendations for urban planners.
Urban Planning
15-Minute City Analysis. For each point, build a 15-minute walking isochrone from key amenities (using OSM data). Heat map of service accessibility reveals 'empty' zones needing a school, clinic, or park. Recommendations are integrated into the master plan.
Population Flow Modeling. Aggregated anonymized mobile operator data builds origin-destination matrices for transport route planning and social facility placement.
How Is Data Security Ensured?
The platform uses encryption at rest and in transit, role-based access control, event auditing. Mobile operator data is aggregated and anonymized. Deployment architecture — private cloud or on-premise, Kubernetes for orchestration, PostgreSQL for relational data, S3-compatible storage for raw data.
Step-by-Step Integration Process of the AI Platform
- Data audit: identify all sources, formats, and update frequencies.
- Data bus design: configure Apache Kafka with appropriate partitions and topics.
- Stream processing development: create Flink jobs for each event type.
- AI module implementation: train models on historical data, configure correlation and forecasting.
- Load testing: verify throughput and latency.
- Deployment and monitoring: deploy to production environment, set up 24/7 monitoring.
What Is Included in Platform Development?
| Stage | Result |
|---|---|
| Analytics | Data source audit, requirements specification |
| Architecture | Design documentation, infrastructure diagram |
| Integration | Connect sensors, configure Kafka, Flink |
| AI modules | Develop models (NLP, forecasting, CV) |
| Testing | Load testing, UAT |
| Deployment | Deploy to production environment |
| Training | Training for operators and analysts |
| Support | 24/7 monitoring, SLA maintenance for 6 months |
The development cost is calculated individually.
Contact us for a preliminary assessment of your city — we will prepare a commercial proposal within 2–3 days. Order a pilot project: in 4–6 weeks we will build a prototype on your data and demonstrate results on real scenarios.
Source: Smart city