Transportation is being transformed by AI at every level—from real-time traffic management in smart cities to decision-making algorithms in autonomous vehicles. Chinese LLMs like DeepSeek-V4, GLM-4, and Qwen3 are playing an increasingly important role in this transformation, powering everything from route optimization to incident analysis.
Through TokenEase's unified API, transportation companies and smart city operators can integrate advanced AI capabilities without building expensive ML infrastructure or managing multiple AI provider contracts.
Process massive traffic sensor datasets with Qwen3's 128K context, generate Chinese navigation and public transit communications with GLM-4, and build complex routing and decision models with DeepSeek-V4's reasoning—all through a single API at 40% lower cost.
Whether for logistics fleets, ride-sharing services, or delivery operations, route optimization directly impacts fuel costs, delivery times, and customer satisfaction. LLMs can synthesize real-time traffic, weather, vehicle constraints, and delivery windows to generate optimal routes with natural language explanations.
import requests
route_context = """
Fleet: 12 delivery vans (Beijing urban distribution)
Depot: Daxing Logistics Park
Operating Hours: 06:00-22:00
Vehicle Constraints:
- Max capacity: 150 packages / 800 kg
- Electric range: 280 km (need 20% reserve)
- Average speed: 25 km/h (urban with traffic)
Deliveries (Aug 28):
1. Chaoyang CBD - 23 pkgs - time window 09:00-11:00
2. Haidian Tech Park - 18 pkgs - time window 10:00-12:00
3. Xicheng Financial District - 31 pkgs - time window 14:00-16:00
4. Tongzhou New Area - 15 pkgs - flexible
5. Shunyi Airport Zone - 12 pkgs - time window 08:00-10:00
6. Fengtai Industrial Park - 27 pkgs - time window 13:00-15:00
7. Daxing Airport - 8 pkgs - time window 16:00-18:00
Traffic Conditions (predicted):
- Morning rush: 06:30-09:30 (2x normal travel time)
- Evening rush: 17:30-20:00 (2.5x normal)
- Airport expressway: Construction delays +30 min
"""
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 optimized delivery routes for vehicle fleets. Consider time windows, vehicle capacity, traffic conditions, fuel/charging constraints, and priority deliveries. Output route sequences with ETAs and load assignments."},
{"role": "user", "content": f"Optimize routes for:\n{route_context}"}
],
"temperature": 0.3,
"max_tokens": 2500
}
)
routes = response.json()["choices"][0]["message"]["content"]
print(routes)Traffic management centers need to predict congestion before it forms and implement countermeasures—ramp metering, signal timing adjustments, and incident response routing. LLMs can analyze multi-source data to predict traffic patterns and recommend interventions.
import requests
traffic_context = """
City: Shenzhen
Date: Friday, August 28, 2026
Time: 17:00 (Evening rush beginning)
Current Conditions:
- Shennan Boulevard: Speed 15 km/h (normal: 45), congestion 8km
- Beihuan Expressway: Speed 35 km/h (normal: 80), accident at Exit 12
- Binhai Avenue: Speed 40 km/h, volume +30% vs normal Friday
- Metro Line 1: Delay +5 min due to signal issue
Events Today:
- 19:30: Concert at Shenzhen Bay Sports Center (capacity 20K)
- 18:00-20:00: Typhoon warning (Category 1, rain intensifying)
- Ongoing: Metro Line 4 maintenance (reduced frequency)
Historical Patterns:
- Friday evening peak: 17:30-19:30
- Concert events: +40% traffic in venue radius 2 hours before
- Rainy Fridays: 25% more private vehicle usage
"""
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 traffic predictions and management recommendations. Forecast congestion hotspots, estimate delays, and suggest signal timing adjustments, VMS messages, and incident response. Consider weather, events, and transit disruptions."},
{"role": "user", "content": f"Predict traffic for:\n{traffic_context}"}
],
"temperature": 0.3,
"max_tokens": 2000
}
)
traffic_forecast = response.json()["choices"][0]["message"]["content"]
print(traffic_forecast)While AVs use specialized perception and control systems, LLMs can assist with high-level decision-making—interpreting complex traffic scenarios, generating human-readable explanations of AV actions, and handling edge cases that require reasoning beyond pure sensor data.
import requests
av_scenario = """
Scenario: Urban intersection with mixed traffic
AV Position: Approaching 4-way intersection, green light
Speed: 35 km/h
Weather: Light rain, road slightly wet
Detected Objects:
- Pedestrian: Elderly person with walker, crossing against signal (left)
- Cyclist: Delivery rider, running red light from right (speed 20 km/h)
- Vehicle: Truck ahead, braking suddenly (brake lights on, decelerating)
- Construction: Lane closure ahead, cones narrowing road
Traffic Rules:
- Green light: AV has right of way
- Pedestrians: Must yield even if crossing illegally
- Cyclists: Treated as vehicles at intersections
"""
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": "Analyze autonomous vehicle scenarios and recommend safe actions. Consider traffic laws, safety priorities, and risk minimization. Provide reasoning that can be logged for regulatory compliance and incident analysis."},
{"role": "user", "content": f"Analyze this scenario:\n{av_scenario}"}
],
"temperature": 0.2,
"max_tokens": 2000
}
)
av_decision = response.json()["choices"][0]["message"]["content"]
print(av_decision)Commercial fleets require careful management of vehicle utilization, maintenance schedules, driver assignments, and regulatory compliance. LLMs can analyze operational data to optimize fleet performance and reduce downtime.
import requests
fleet_data = """
Fleet: 25 long-haul trucks (Shanghai-Beijing corridor)
Vehicle Age: 2-5 years
Mileage: 80K-450K km
Recent Issues:
- Truck T07: Engine warning light, oil pressure fluctuating
- Truck T12: Brake pad wear at 15% (replacement threshold: 10%)
- Truck T18: Tire pressure sensor malfunction
- Truck T23: Refrigeration unit inefficiency (+15% fuel consumption)
Operational Requirements:
- All trucks must pass annual inspection by Sep 15
- Peak shipping season starts Sep 1 (demand +40%)
- Maintenance bay capacity: 3 trucks simultaneously
- Average service time: 4 hours routine, 8 hours major
- Spare parts lead time: 2 days for common, 5 days for specialized
"""
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 fleet maintenance schedules that minimize downtime, ensure regulatory compliance, and prepare for peak demand periods. Prioritize safety-critical repairs, optimize shop utilization, and manage parts inventory."},
{"role": "user", "content": f"Schedule maintenance for:\n{fleet_data}"}
],
"temperature": 0.3,
"max_tokens": 2000
}
)
maintenance_schedule = response.json()["choices"][0]["message"]["content"]
print(maintenance_schedule)Understanding accident causes is essential for prevention. LLMs can analyze accident reports, traffic camera data, and vehicle telemetry to identify patterns, root causes, and recommend infrastructure or policy improvements.
import requests
accident_data = """
Location: Hangzhou-West Lake Avenue & Jiefang Road intersection
Period: Jan-July 2026 (12 accidents)
Accident Summary:
1. Rear-end collision (3 cases) - red light running
2. Pedestrian struck (4 cases) - crossing during turn signal
3. Side-impact (3 cases) - failure to yield on left turn
4. Cyclist collision (2 cases) - right turn without checking
Common Factors:
- Time: 80% occurred 17:00-19:00 (evening rush)
- Weather: 60% during rain or dusk (low visibility)
- Speed: Average approach speed 55 km/h (limit: 50)
- Signal timing: Yellow light 3 seconds (standard: 4-5)
- Signage: No "No Turn on Red" sign for right turns
Infrastructure:
- 4 lanes each direction
- No dedicated bike lane (cyclists share right lane)
- Pedestrian crossing: 12m wide, no refuge island
- Sight distance: Adequate (>150m)
"""
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": "Analyze traffic accident patterns and recommend prevention measures. Consider engineering solutions, enforcement strategies, education campaigns, and policy changes. Prioritize by cost-effectiveness and implementation feasibility."},
{"role": "user", "content": f"Analyze accidents at:\n{accident_data}"}
],
"temperature": 0.2,
"max_tokens": 2000
}
)
accident_analysis = response.json()["choices"][0]["message"]["content"]
print(accident_analysis)Urban parking is a $100B global problem. LLMs can optimize parking space allocation, predict demand patterns, and guide drivers to available spots—reducing congestion, emissions, and driver frustration.
import requests
parking_context = """
Facility: Shanghai Pudong Lujiazui Underground Parking
Capacity: 2,000 spaces across 4 levels
Current Occupancy: 1,420 spaces (71%)
Time: 14:30 Tuesday
User Types & Durations:
- Office workers: 08:00-18:00 (65% of capacity, predictable)
- Shoppers: 2-4 hours (20%, peak weekends)
- Short-term: <1 hour (15%, highly variable)
- Monthly subscribers: 800 spaces (reserved)
Pricing:
- First hour: CNY 15
- Each additional hour: CNY 10
- Daily max: CNY 80
- Monthly: CNY 1,200
Current Issues:
- Level B1: 95% full (office zone, peak occupancy)
- Level B4: 45% full (furthest from elevators)
- EV charging: 50 stations, 38 occupied, 6 queueing
- Handicap spaces: 40 total, 12 occupied (non-handicap vehicles observed)
"""
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 parking facility operations. Recommend dynamic pricing, space allocation strategies, signage improvements, and technology upgrades. Consider user experience, revenue maximization, and accessibility compliance."},
{"role": "user", "content": f"Optimize parking for:\n{parking_context}"}
],
"temperature": 0.3,
"max_tokens": 2000
}
)
parking_optimization = response.json()["choices"][0]["message"]["content"]
print(parking_optimization)Integrate DeepSeek-V4, GLM-4, and Qwen3 into your transportation platforms, fleet systems, and smart city infrastructure. Get started today →