Developing an AI system for GovTech and Smart City
GovTech uses AI to improve public administration efficiency and create better cities. Key objectives include personalized government services, security, situational analysis, and urban analytics.
Intelligent Security Platform
Video Analytics for city cameras:
City CCTV camera network + real-time AI analytics:
from ultralytics import YOLO
import cv2
import numpy as np
from collections import defaultdict
class CitySecurityAnalytics:
"""Видеоаналитика городских систем безопасности"""
def __init__(self, detection_model='yolov8n.pt'):
self.model = YOLO(detection_model)
self.crowd_threshold = 50 # порог для crowd detection
self.loitering_threshold = 120 # секунд для детекции праздношатания
def analyze_frame(self, frame, camera_id, timestamp):
results = self.model.track(frame, persist=True, classes=[0]) # 0=person
alerts = []
person_count = len(results[0].boxes) if results[0].boxes else 0
# Детекция скопления (crowd)
if person_count > self.crowd_threshold:
alerts.append({
'type': 'crowd_detected',
'camera_id': camera_id,
'count': person_count,
'severity': 'warning' if person_count < 100 else 'critical'
})
# Детекция оставленных предметов
# (требует трекинга: предмет без владельца >60 сек)
abandoned = self._check_abandoned_objects(results, timestamp)
if abandoned:
alerts.append({'type': 'abandoned_object', 'camera_id': camera_id})
return {'person_count': person_count, 'alerts': alerts}
Predictive Policing Analytics:
Prediction of high-risk areas based on historical data: - Spatio-temporal crime patterns - Factors: type of area, time of day, day of the week, major events - Kernel Density Estimation + ML → hot spot maps - Ethical restrictions: use only for patrol planning, not individual scoring of citizens
Urban transport analytics
Traffic light control (Adaptive Traffic Control):
AI adapts the duration of traffic light phases in real time: - Video detection of queues at each entrance - Reinforcement Learning: the agent manages the phases → reward = minimization of the total waiting time - Priority: public transport, ambulance (Vehicle to Infrastructure)
Traffic forecast:
Graph Neural Network on the city road graph: - Nodes: intersections and road segments - Features: historical traffic, time of day, weather, events - Horizon: 15–60 minutes → warning for drivers and navigation systems
Digital government
Intelligent routing of citizen requests:
ERZIAN/ESIA + NLP processing of requests: - Classification: request type, agency, priority - Routing to the required executor - Duplicate detection: similar requests from different citizens on the same issue → aggregation
Predictive analytics for planning:
- Forecasting demand for government services: planning MFC capacity - Queues: ML-based management of appointments (like a doctor's appointment, but for government services) - Tax analytics: forecasting budget revenues
NLP search by regulatory framework:
Municipal lawyer, citizen, entrepreneur: semantic search by regulatory legal acts: - Embedding-based retrieval + RAG: "What are the requirements for parking spaces during shopping center construction?" - The system responds with citations from SP, GOST, and local regulations
City Digital Twin
Operational double of the city:
3D city model (CityGML, Cesium) in real time: - Data from IoT sensors, transport systems, weather stations - Visualization of layers: transport, ecology, housing and communal services, security - Situation center: dashboard for the duty officer
Scenario Modeling:
What-if analysis for urban solutions: - "What will happen to traffic when the Garden Ring is closed for repairs?" - "How will the environment change when 30% of buses are converted to electric?" - Agent-based modeling + ML forecasting
Development timeline: 8–16 months for a comprehensive GovTech platform with video analytics, transport management, and City Digital Twin.