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A Deeper View of AI Enabled Innovations and Technologies in Transit
YOUSEF KIMIAGAR, MMSc., P.Eng, PMP, FIRSE
Rail and Transit Systems
Toronto, Canada
+1 416 670 8740
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New Ecosystem
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Reshaping our life
Altering the way we live - work - interact
Unprecedented scale, scope, complexity of transformation
Exponential rate - Natural and Artificial
Digital - Physical - Biological Boundaries
Interconnectedness - collaboration, partnership, sharing the knowledge and innovation
I3.0 vs. I4.0
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Industry 4.0
Better
Faster
Cheaper
Predictions
Industry 3.0
Better
Faster
Cheaper
Arithmetic
Hype Cycle
• Emerging Technologies• Innovation trigger
• Peak of inflated expectation
• Trough of disillusionment
• Slope of enlightenment
• Plateau of productivity
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Source: Gartner
Hype Cycle
• Emerging Technologies• Innovation trigger
• Peak of inflated expectation
• Trough of disillusionment
• Slope of enlightenment
• Plateau of productivity
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Source: Gartner
AI History
Source: Digital Wellbeing
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1950 Alan Turing
1955 John McCarthy
1977 Deep Blue
2002 Roomba
2011 Siri
2011 Watson
2014 Alexa
2017 Alpha Go (2170) or 1.5 x 10Exp51
Source: Adobe
Dystopian
Or
Utopian
SymbioticParasitism - Mutualism
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Supperminds
Hierarchies
Group decisions
ML as members of the group – routine
work of the org
Top people make non routine
decisions
Machine supervising people
Machine assigning work to individuals
Democracies
ML to participate in decision making by
voting
Weights of the votes depending on
the accuracies in the past
Markets
Internal markets-who works on what
What projects to bid
Trading on behalf of human
Communities
Informal consensus
ML to participate in the communities
Wikipedia –informal consensus
Machines to participate in the
Wikipedia –automated Bots
Reimagining Work
• Real-Time problem solving (High Skill/High Wage)
• Reasoning• Intuition• Self Aware• Consciousness• Judgment• Insight
• Standardized, Codified, Routinized
• Hands-on (Low Skill/Low Wage)
• Unstructured
High/Soft Skills
Difficult for Machine
Low Skills
Difficult for Machine
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More new jobs expected
AI Field
AI
Machine
Learning
Deep Learning
Supervised
Unsupervised
NLPRobotics
Content Extraction
Classification
Machine Translation
Question Answering
Text Generation
Vision
Image Recognition
Machine Vision
Speech
Speech to Text
Text to Speech
Planning
Expert Systems
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AI
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• Data explosion (Zeta Bytes): 44x1021
• Massive processing power • 12 x 10 12 Calculation/Sec
• ANI: Artificial Narrow Intelligence• Solving specific problems: Deep Blue, Alpha Go, etc.
• AGI: Artificial General Intelligence• Human level intelligence• Imagine things never seen before• Plan/solve problems to make those things real• Learning & building new models about the world• Intuitive psychology• Lear to learn
• ASI: Artificial Supper Intelligence (Singularity)
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Breakthroughs
Hardware
• Moore’s Law:
• Microchip speed/power doubling every 2 years
• 3D chips, 40 GPUs on a chip
Processing
Speed
• Processing speed
• MHz to GHz
Analytics • AI/ML
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Neural Networks
Source: Research Gate
• First Neuron Network• 1958 Frank Rosenblatt
• DNN: Deep Neural Network
• CNN: Convolutional NN• Image recognition
• RNN: Recurrent NN• Modeling sequence data• Speech recognition, language translation,
stock prediction
• LSTM: Long Short-Term Memory NN• Time series data• Passenger flow prediction• Traffic speed prediction• Movement trajectory pattern mining
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ML Pattern Recognition
Supervised Classification
Supervised Classification – Support Vector Machine
Clustering: Supervised/Unsupervised
Supervised ML
• Feed in labeled data
• Train the algorithm
• Feed in the test data
• Adjust the algorithm
• Improve the output accuracy
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DNN - Image Recognition
Source: 3blue1brown
Transit Digitalization
Cognitive Technologies – Responsive - Agile
Creating Integrated Ecosystem
Predicting Delays and Service Disruptions
Condition Monitoring/Predictive Maintenance
Extended Factory Boundaries
Faster and More Flexible Manufacturing/Testing
Smarter/More Sustainable Products
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Train Control
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SIL0: non-safety
Could be Probabilistic
AI Based:
Arrival time
Timetable
Ride comfort
Train regulation
SIL4: safety critical (max hazard 10-9)
Must be Deterministic
AI Assisted:
No direct control
Advisory role to SIL4
•Position
•Acceleration
•Safe braking
•Interlocking
Improved EfficiencyCBTC + SIL0 AI
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Source: IRSE News 258: Alexandre Pires
Path to Autonomous
21
• Autonomous LRV in Potsdam, Germany
• Three stage to fully autonomous operation
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Source: Siemens Mobility
Simulator & real environment testing
Obstacle detection
Improve safety increase capacity
Improve energy consumption
Intelligent Schedule Optimization
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Learning from historical data
Intelligent energy management
Charge cycle/locations
Electricity price/demand
Predicting on-time performance
Optimizing/improving schedule
Costs tradeoffs vs. on-time performanceSources: Proterra/Optibus
• Beijing subway network case study
• Time-dependent, passenger demand-driven timetable synchronization/optimization
• Optimized travel time in a network
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Dynamic Timetable
TRB Journal 2018
• Adjusting departure times, running times, stopping times, and headways of all trains on each line
• Multi-objective - Pareto optimum schedules
• Considering infrastructure capacity, passenger satisfaction, cost optimization
• AI Techniques:• Neural networks• Genetic algorithms (GA)• Simulated annealing• Tabu search algorithms
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Schedule Synchronization & Optimization
TRB Journal 2018
• Complex station with terminus platform
• Multiple routes – high/low speed train operation
• Similar pattern of improvement in capacity, operation robustness, punctuality
• Changes to track layout and/or locations of signal boxes fault tolerant rules
• AI methods used to optimize timetables in the implementation of the fault tolerant rules
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A Fault Tolerant Design
Source: University of Salford in collaboration with the Institute for Transport Studies at the University of Leeds.
AFC
• Bring your own ticket
• SaaS, Cloud Computing
• Account based ticketing
• Best fare finding algorithm
• Time dependent fare
• MaaS: seamless transfer first/last mile
• Accessibility and discoverability
• Cloud native scalability
• Continuous improvements
• Minimizing the infrastructure
• AI based biometric payment
Source 1: masabi
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Source 2: Biometric
Energy Conservation
27
Source: Thales
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Intelligent scheduling
Speed optimization maximizing
coasting
Maximizing regenerative
braking efficiency
Create driving profiles &
computerized instructions
Efficiency gains 15% reduction in
energy consumption
Smoother operations –
reduced wear on track and trains
Energy control adjust peak energy
demand spikes
• One mile/hour
• Saves 5,000 cars
• 250 locomotives
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IBM Smarter Rail
Source: IBM Smarter Rail
Dynamic scheduling
Surveillance of track and
infrastructure
Predictive maintenance
Integration with road,
sea, and air travel
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GE Movement Planner
• GE’s RailEdge® Movement Planner breakthrough
• Predicting patterns in train traffic
• Reduced environmental impact
• Increased capacity, velocity, efficiency
• Average network speed up 10-20%
Source: Norfolk Southern
US freight doubles in 25 years
Every mile speed increase$200m CapEx savings
Data Driven Maintenance - SNCF
Source: OSIsoft
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Watson AI + Remote Sensors
30,000 km, 15,000
Trains/day
50% up in 10 years
Real time Data
Processing
Sequential ML
Automatic Alerts
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AI Optimized Simulation Modeling
Source: AnyLogic
Learn more in a day than a
professional in lifetime
Train to learn from thousands of simulations
Construct models using algorithms
that learn and update in real time
Learn from past, optimize and
calibrate in virtual world
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Prediction & Prevention
Source: Wi-Tronix
Records locomotive & video data, available in real time
Real time locomotive status and response
Artificial intelligence & machine learning
Live visual intelligence /real time status
Early identification of health issues
Increased safety/decreased maintenance costs
Machine Vision/Learning
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• Since 2002 (Nebraska, Iowa and Arkansas)• Thousands of Sensors
• Cameras, LIDARs, Laser
• 50,000 Images/Sec
• 360 Laser View
• Machine vision
• Machine learning
• Maintenance schedule
• Increased safety
• Reduced costs
Source: UP
• Connected Intelligence (AI/ML/NN)
• Truevue360 – AI division
• Intelligent 360 imagery
• Situational awareness inspection processes & security
• CN portals Winnipeg:• Machine vision• Predictive analytics
• $400 million savings (2020 to 2022)
• AI based inspection 120 times faster than conventional
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Railcar Inspection Portal - rip®
Source2: duostech
Source1: duostech
Source 3: Financial Post
Indian Railway
• 4th largest rail network
• 70,000 km track
• 20,000 passenger trains
• 9,000 freight trains
• 7,500 stations
• AI robot will check for faults in trains
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Source1: AI for Indian RailSource 2: Scroll in
SEMMI
• Deutsche Bahn customer service agent
• Berlin's main railroad station
• 300,000 passengers/day
• AI based Intelligent assistant
• Cloud-based voice user interface
• German, English + 7 other languages
• Socio- Empathetic Human- Machine Interaction
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Source: IEEE Spectrum Vol. 56 , Issue: 8, Aug. 2019
Outlook
1 out of 20
Agree that AI is a strategicopportunity for theirorganization
Extensively Incorporated AI
1 out of 5
Agree that AI is a strategicopportunity for their organization
Incorporated Some Form of AI
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Photo: Accenture
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Implementation Plan
Target the use case
Cost savings: what, where
Cost of the (structured)
data
Acquire expertise and talent
Computer scientists
structure the data
Mathematicians create the algorithms
IT specialist preserve the
system
Domain experts (business
knowledge)
Imagine what data might be
useful
Understand the change in the business
processes
Continuously train the
algorithms
Architect the data
Data scientists to structure
data
Conclusion
Technology no longer a constraint – need imagination and supporting business case
Intelligent Automation
Collaborative problem solving
Act fast, embrace failure, value conflict
Decentralized organizations and looser hierarchy
Digital disruption – Digital maturity
Reskill and empower employees
Collective learning
Leadership and vision
How prepared you are in transforming your organization!?
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THANK YOU
YOUSEF KIMIAGAR, MMSc., P.Eng, PMP, FIRSE
Rail and Transit Systems
Toronto, Canada
+1 416 670 8740
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