By 2050, nearly 70% of the world's population will live in cities. Managing urban complexity—traffic, pollution, public safety, housing, and infrastructure—requires intelligent systems that can process vast amounts of data and generate actionable insights. Chinese LLMs like DeepSeek-V4, GLM-4, and Qwen3 are emerging as critical tools for smart city operators and urban planners.
Through TokenEase's unified API, city governments and urban planning agencies can deploy AI solutions without building specialized data science teams or managing multiple AI vendor relationships.
Process massive urban sensor datasets with Qwen3's 128K context, generate Chinese public communications and policy documents with GLM-4, and build complex urban models with DeepSeek-V4's reasoning—all through a single API at 40% lower cost.
Traffic congestion costs cities billions in lost productivity and pollution. LLMs can analyze real-time traffic data, public transit schedules, event calendars, and weather conditions to optimize signal timing, recommend route changes, and coordinate multi-modal transportation.
import requests
traffic_context = """
Intersection: Nanjing Road & Huaihai Road, Shanghai
Time: 08:30 (Morning Rush)
Current Conditions:
- Northbound queue: 28 vehicles (backed up 200m)
- Southbound queue: 15 vehicles
- Eastbound queue: 42 vehicles (backed up 350m)
- Westbound queue: 22 vehicles
- Pedestrians: 150/minute crossing (4 directions)
- Bicycle lane: 80 cyclists/minute
Signal Timing (current):
- North-South green: 45 seconds
- East-West green: 35 seconds
- All-red clearance: 5 seconds
- Cycle length: 90 seconds
Nearby Events:
- Metro Line 2 station: Morning entry peak (2,000 passengers/15min)
- Office buildings: 08:30-09:00 arrival window
- School zone 200m west: Drop-off period
- Construction: Lane closure on Huaihai Road (east approach)
Public Transit:
- Bus routes 20, 37, 921 serving intersection
- Bus frequency: 3-5 minutes (delayed due to congestion)
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "Optimize urban traffic signal timing based on real-time conditions, pedestrian flows, transit integration, and special events. Consider safety, equity, and multimodal transportation. Provide specific timing recommendations with expected impact."},
{"role": "user", "content": f"Optimize signals for:\n{traffic_context}"}
],
"temperature": 0.3,
"max_tokens": 2000
}
)
signal_optimization = response.json()["choices"][0]["message"]["content"]
print(signal_optimization)Urban air pollution affects millions of residents. LLMs can synthesize data from air quality sensors, weather stations, traffic patterns, and industrial emissions to predict pollution episodes and recommend mitigation measures.
import requests
environmental_context = """
City: Beijing
Date: August 29, 2026
Current AQI: 142 (Unhealthy for Sensitive Groups)
PM2.5: 52 ug/m3
PM10: 89 ug/m3
O3: 168 ug/m3
NO2: 45 ug/m3
Contributing Factors:
- Traffic: Heavy congestion on 2nd Ring Road (accident at Dongzhimen)
- Weather: High pressure, wind speed 1.2 m/s (stagnant)
- Temperature: 32C (favorable for ozone formation)
- Humidity: 45%
- Industrial: 3 major factories operating at 90% capacity
- Construction: 12 sites active within 5km of monitoring station
Forecast (next 48 hours):
- Aug 29 evening: Wind shift to NW, speed increasing to 4 m/s
- Aug 30: Cold front passage, scattered showers
- Aug 31: Clear, wind 3-5 m/s
Health Advisories:
- 2.3M residents with respiratory conditions
- 180 schools in affected area
- Outdoor events scheduled: Marathon (Aug 30, 06:00)
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Generate air quality forecasts and health advisories. Recommend mitigation measures for city authorities, including traffic restrictions, industrial curtailments, and public health communications. Consider economic and social impacts."},
{"role": "user", "content": f"Forecast air quality:\n{environmental_context}"}
],
"temperature": 0.3,
"max_tokens": 2000
}
)
air_quality = response.json()["choices"][0]["message"]["content"]
print(air_quality)Parks, libraries, community centers, and public toilets require efficient management. LLMs can analyze usage patterns, maintenance needs, and citizen feedback to optimize operations and improve service quality.
import requests
facility_context = """
Facility: Shanghai Central Park (140 hectares)
Annual Visitors: 12 million
Current Issues:
- Restroom queues: Average 8 minutes (weekends)
- Playground equipment: 3 of 12 sets out of service
- Trash collection: Overflowing bins by 14:00 on weekends
- Lighting: 15% of pathway lights non-functional
- Parking: 100% capacity by 10:00 on Saturdays
- WiFi: Complaints about dead zones in east garden
Usage Patterns:
- Peak: Weekends 10:00-16:00 (8,000 visitors/day)
- Morning exercise: 06:00-08:00 (2,000 daily)
- Evening strolls: 18:00-21:00 (3,000 daily)
- Seasonal: Cherry blossom season +40% visitors (Mar-Apr)
Budget: CNY 8M annual maintenance
Staff: 45 maintenance workers
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "glm-4-plus",
"messages": [
{"role": "system", "content": "Optimize public facility management. Analyze usage data, identify bottlenecks, and recommend operational improvements, staffing adjustments, and capital investments. Consider visitor experience and budget constraints."},
{"role": "user", "content": f"Optimize park operations:\n{facility_context}"}
],
"temperature": 0.3,
"max_tokens": 2000
}
)
facility_optimization = response.json()["choices"][0]["message"]["content"]
print(facility_optimization)Natural disasters, accidents, and public health emergencies require rapid, coordinated responses. LLMs can analyze incident data, resource availability, and population distribution to generate response plans and evacuation strategies.
import requests
emergency_context = """
Incident: Flash Flood Warning
Location: Shenzhen River Basin
Time: 14:30, August 28, 2026
Weather Data:
- Rainfall: 120mm in past 3 hours (forecast: additional 80mm)
- River level: 3.2m (flood stage: 3.5m)
- Trend: Rising 0.3m/hour
- Typhoon: Category 1, 80km SW of city, moving NE
Affected Areas:
- Luohu District: 450K residents, 15 residential blocks in flood zone
- Low-lying commercial area: 2,000 shops
- Subway Line 3: 2 stations at risk
- Hospital: Shenzhen People's Hospital (emergency generator available)
Resources Available:
- Emergency personnel: 800 (fire, police, medical)
- Evacuation buses: 50 (capacity: 2,500 people)
- Shelters: 12 locations (capacity: 8,000 total)
- Sandbags: 15,000 (need estimate: 25,000)
- Mobile pumps: 8 (capacity: 500 L/min each)
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "Generate emergency response plans for urban disasters. Prioritize life safety, coordinate multi-agency responses, and manage resource allocation. Include evacuation sequencing, shelter operations, and public communication strategies."},
{"role": "user", "content": f"Develop emergency plan:\n{emergency_context}"}
],
"temperature": 0.2,
"max_tokens": 2500
}
)
emergency_plan = response.json()["choices"][0]["message"]["content"]
print(emergency_plan)Understanding population dynamics is essential for infrastructure planning, service delivery, and economic development. LLMs can analyze census data, migration patterns, economic indicators, and housing trends to forecast urban growth and recommend planning strategies.
import requests
population_context = """
City: Chengdu
Current Population: 21.2 million (2025 census)
Growth Rate: +2.1% annually (past 5 years)
Demographics:
- Age 0-14: 13.2%
- Age 15-59: 67.8%
- Age 60+: 19.0%
- Higher education: 28% of workforce
- Migration: +180K net annually (mostly from Sichuan province)
Economic Indicators:
- GDP per capita: CNY 118K
- Major industries: Electronics, automotive, software
- Employment growth: +3.5% annually
- Housing starts: 85K units/year
- Commercial space: 2.1M sqm under construction
Infrastructure Status:
- Metro: 12 lines, 520km (expanding to 700km by 2030)
- Road network: 85% of planned expressway system
- Water supply: 92% coverage
- Green space: 14.2 sqm per capita (target: 15)
- Schools: 78% utilization (shortage in new districts)
Challenges:
- Traffic congestion index: 1.72 (severe)
- Air quality: 285 good air days/year (target: 300)
- Affordable housing waitlist: 45K families
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Analyze urban population trends and generate growth forecasts. Recommend infrastructure investments, service expansions, and policy adjustments. Consider sustainability, equity, and economic development goals."},
{"role": "user", "content": f"Forecast urban growth:\n{population_context}"}
],
"temperature": 0.3,
"max_tokens": 2500
}
)
growth_forecast = response.json()["choices"][0]["message"]["content"]
print(growth_forecast)Cities have limited budgets for infrastructure improvements. LLMs can analyze condition assessments, usage data, economic impact projections, and maintenance backlogs to prioritize investments that deliver maximum public value.
import requests
infrastructure_context = """
City: Wuhan
Annual Infrastructure Budget: CNY 12B
Backlog: CNY 28B in identified needs
Candidate Projects:
1. Bridge Rehabilitation: Yangtze River Bridge #2
- Cost: CNY 2.1B
- Condition: Fair (structural concerns)
- Daily traffic: 120K vehicles
- Risk: Closure would add 45 min to commutes
- Economic impact: CNY 800M/year in avoided delays
2. Metro Line Extension: Line 8 Phase 3
- Cost: CNY 8.5B
- Length: 18km, 12 stations
- Ridership forecast: 450K daily
- Coverage: 3 underserved districts
- Construction: 4 years
3. Sewage Treatment Upgrade
- Cost: CNY 1.8B
- Capacity increase: +40%
- Environmental benefit: Improve river water quality
- Regulatory: Required by 2027
4. Road Resurfacing Program
- Cost: CNY 800M
- Coverage: 280km of arterial roads
- Condition: 35% rated "poor"
- Safety: 120 accidents/year on poor roads
5. Public WiFi Expansion
- Cost: CNY 200M
- Coverage: Add 2,000 hotspots
- Digital equity: Serve 500K low-income residents
"""
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "glm-4-plus",
"messages": [
{"role": "system", "content": "Prioritize urban infrastructure investments using multi-criteria analysis. Consider safety, economic impact, equity, regulatory requirements, and strategic alignment. Provide ranked recommendations with justification."},
{"role": "user", "content": f"Prioritize projects:\n{infrastructure_context}"}
],
"temperature": 0.3,
"max_tokens": 2500
}
)
prioritization = response.json()["choices"][0]["message"]["content"]
print(prioritization)Integrate DeepSeek-V4, GLM-4, and Qwen3 into your smart city platform. Get started today →