The document discusses how big data and artificial intelligence can enable smart traffic management. It provides examples of how AI cameras, cellular data collection from mobile apps, and edge computing can be used with machine learning algorithms to analyze traffic patterns, detect violations, and optimize traffic signal timing. The results include reducing traffic delays, improving traffic flow, increasing road safety, and enhancing law enforcement efficiency.

1. 1
Big Data & AI
Smart Traffic Management
Jorge Sebastiao, CISSP
CTO Ecosystem
DCSS
Twitter.com/4jorge
Linkedin.com/in/sebastiao

2. 2
Human Society Is Entering the Intelligent Era
Data
Generated
Data
Stored
Data
Processing
Actionable
Insights
Big Data Artificial Intelligence
Structured and
unstructured data
Data is stored in
databases and servers
Process the data using
CPU/GPUs and AI
algorithms to detect
patterns
Predictive signals are
generated
ICT infrastructures have become the cornerstone of the smart world.

3. 3
Public safety Insurance Education Retail Health Manufacturing Media Power Pharmacy Oil&Gas Logistic Finance
Big Data + AI Drive Enterprise Transformation
Network
Intelligence
Enterprise
Intelligence
Smart
Things

4. 4
Big Data + AI driven by Connectivity
Storage, Computing & Data Centers

5. 5
FusionInsight Big Data platform
Data service
Customer
investigation
RecommendationDetails (images) Pseudo-control
Data collection Data integration
Data exploration
analysis
Result displayData analysis
Agile
Smart
Intelligent
Offline/Near-line
computing
Memory computing Real-time stream
computing
Parallel database
MPPDB
Big Data Platform extracts Intelligence

6. 6
AI needs new Processors
Theoretical Basis Internal Practice
Chip Device Cloud
External Enabler
(EI services)
Noah Ark
Lab
Distributed
Lab
Neumann
Lab
Shannon
Lab
GTS ISC+
Device BP&IT
Heterogeneous computing
General AI
Essential platforms
Domain-specific
solutions

7. 7
AI Cloud enables Connected Intelligent Enterprises
Collocated
storage
Device-Cloud
collaboration
HW-SW
synergy
Easy to
Use
Converged
decision
Full lifecycle
support
Open &
compatibility
AI Platform Services

8. 8
Full Stack Artificial Intelligence enables Simplified AI
Feature ranking
Feature
engineering
Model tuning
AutoFE & Model Optimization
Full-stack AI capability
Big data base
Intelligent Decision
Essential Platforms
Generic AI Services
Rich services ready to use
Full Lifecycle Support
Train
Evaluate
Serving
Deploy
Upgrade

9. 9
AI Enables full Enterprise solutions
Intelligent Twins
 TrafficGo: Traffic optimization, safety,
trend analysis
 iWater: Sewage treatment, environmental
monitoring, river safety
City
 Production quality: Flexible quality
classification, predictive maintenance
 Sales forecast: Regional, product, or
combined
 Production planning: Material forecast
Manufacturing
 Customer analysis, customer profile,
customer forecast, VIP analysis
 Abnormal event monitoring: Traffic
surge monitoring, illegal behavior (stay/wander)
Campus
 Inventory management,
shipping path planning
 Trunk dispatching, smart
entrucking
 Intelligent declaration: OCR,
customs price forecast
Logistics
 Video recommendation:
user profile, video tag, video
classification
 Content inspection:
Pornography, terrorism, violence...
Internet
 Fleet management: Electric fence,
trajectory mining, path planning
 Autonomous Driving: MDC, V2X,
Scheduling
Vehicle
 Home Safety: Stranger detection
 Family time: Smile moment, pet identification
 Elderly care: Fall detection, cry detection,
abnormal sounds
Home
 Pathological analysis: Cervical cancer analysis,
lung cancer tissue analysis, pathology report generation
 Genetic analysis: Gene sequencing, disease
classification and prediction based on genetics
Health

10. 10
City Traffic Challenge

11. 11
Schenzhen Urban Transportation Challenges
---Congestion, Violation, Management
• Violation Types: Illegal Parking、
Improper Lane Use、Crossing
Line(Driving) and Wrong Way(Driving)
• Violation Number： ~300k Case per
Day in X city
• Large Volume of Vehicle：~6
Millions Vehicles in X city.
• Congestion：The average speed is
~22km/hour in the rush hour。
Severe Congestion Frequent Traffic Violation Low efficiency Management
• Workload： ~300k image capture
per Day, but only process 30k-50k
due to workload
• Scope： Lots of Blind Spot. Non-
AI camera.

12. 12
AI Traffic Key Elements

13. 13
Smart Traffic Management Sensors
city
district
intersection
Hierarchical & Crowd
Intelligent Decision
lights
truck
camera RLS
MLSGES
Converge
DLS
Smart Transportation
Supply Chain
Computer Vision

14. 14
AI Cameras. Salik Gates, AI APP

15. 15
Cellular Tower &
Mobile App Collecting

16. 16
NB-IoT enables new AI Big Data Collecting
CoAP
Smart HomeAgricultureSmart Tracking EnvironmentSmart Parking Smart Metering Vehicle
IoT Connection Management Platform
Application
Enablement
Connectivity
Management
API Gateway
Service
Orchestration
SIM
Management
Device
Management
Security &
Identity
Rule Engine Big Data
BSSForIoT
Connectivity
PSM Multi Profile
IoT Agent Inside
16

17. 17
AI - Machine Learning
IntrafficIn-traffic
In-traffic

18. 18
AI learns from patterns

19. 19
before after
Comparison before and after global traffic optimization
Travel Time Delay Wait Time # of Stops

20. 20
Machine Learning

21. 21
Driver Profiling

22. 22
Multimode AI
Smart Response

23. 23
Integrate to 10+ algorithm vendors
with 20+ AI application.
Shenzhen Traffic Police Smart Traffic Brain
Cluster resource utilization
improved by 100%
Illegal
occupation
Red light
Signal indicator
optimization
Video event
Check
License plate
recognition
Roll back the
scrap.
Shenzhen Traffic Brain
... ...
5x
Efficiency improvement
10%
Traffic improvement
Urban road network signal light
Control and optimization
Automatically identify traffic
violations
analysis
algorithms
Video/Image
data source
Analysis
Result
Result
convergence
Algorithm
Evaluation
FusionMind
Smart Traffic

24. 24
Camera Video
Signal Control Policy
AI Based Signal Control
Offline Variant Cycle Online Adaptive
Video Data Analysis
Deep Learning
Traffic Data Fusion
Huawei AI Platform
pedestrian recognition Vehicle recognition
Edge Computing Cloud Computing
Object Tracking
Deep Learning
Signal Control： Integrate signal control theory with
reinforcement learning to support big area signal control.
Edge Computing：Great cost performance with Huawei AI
chip
Video Analysis：Analyze the pedestrian, Vehicle and Road
Structure by deep learning; bird's-eye view video
Concise Traffic Data： Fuse the structured data from
traditional device with video.
Edge Computing
Enable Smart Traffic Flow using AI Cameras, lights
Structured Data
from Other
Device
Lane recognition Tiny Object recognition
Image Enhancement
Passenger
Car Unit
Diverging
Ratio
Vehicle
Speed
Queue
Length
Kalman Filtering
Isolated Control
Arterial Coordinated
Control
Regional
Coordinated Control
Reversible Lane
Control

25. 25
LawEnforcementProcess
Checkpoint
Camera
Red Light
Camera
Human Check
data
Violated
Passed
Double Check
ViolationCheckby
AI
Algorithm
Repository
….
AI Platform
Data Input
Law Enforcement with AI
Feature Extraction Violation Check Double Check
Application
LawEnforcementEcosystem
 Workload： ~300k image capture per Day, but only
process 30k-50k due to workload
 Efficiency: ~70-80% images can not pass the check.
 Algorithm： Deep learning based law enforcement.
Algorithm Repository
 Platform： FusionMind AI Platform
Solution：FusionMind AI
Challenges
 Reduced Workload： Reduce 30k case to 6000 case
for human check.
 New Violation： Modeling time( Reduced from two
month to one day), Process time(Reduced from one
day to 5 minutes)
Business Value
Smart Law Enforcement using AI
Feature
Extraction
Image Record
For Violation
Not Violated
Truck Restriction
Local Vehicle Only
No License Plate
Improper
Lane Use
……….
Truck Restriction
Local Vehicle Only
No License Plate

26. 26 AI Value
Simplify
Complexity
Multi
Response
Continuous
Learning
Agility

27. 27
Smart Transportation
• Autonomous Driving Car:
• Audi Q7 Autonomous Driving Prototype
• Internet of Vehicles
• Based on Huawei OceanConnect
• PSA DS7 Crossback
• PATEO
• 5G LTE-V
• Collaborated with BMW
• V2X: V, I, N, P
• Verified in WuXi, China

28. 28
• Launched at Sep, 2012
• Focused on CT network
• Launched at Jan, 2015
• Focused on NFV solution
• Launched at Jan, 2015
• Focused on SDN solution
• Launched at Apr, 2016
• Focused on DC solution
Operations Transformation & Network Evolution
Shenzhen/Beijing Xi’an Beijing LangFang
Pre-Integration & Pre-Verification Joint InnovationEcosystem Establishment
• Focus on WEU operator
joint innovation
Düsseldorf
Huawei Open Lab: Integration & Innovation Center
Paris
Munich

29. 29
Дякую
Jorge Sebastiao, CISSP
CTO Ecosystem
DCSS
Twitter.com/4jorge
Linkedin.com/in/sebastiao