Connected Vehicles & Smart
Transportation
(CVST)
Ali Tizghadam, Agop Koulakezian, Alberto Leon-Garcia
Department of Electrical & Computer Engineering
University of Toronto
4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 1
Outline
ITS: Past, Future
New Players, Enablers
CVST proposal
4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 2
CVST proposal
High-Level Architecture
Early Results
Issues in Intelligent Transportation Systems
Existing issues
Traditional transportation issues (grid lock, freeway congestion, transit delay,
safety, etc)
Computational issues
How to address the issues?
More intelligence
Enhanced computing (Cloud)
Enablers
Rich GPS mobility data from personal devices
4G LTE vehicle-anywhere connectivity
4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 3
Computational issues (processing time, processing
power)
Data management issues (data-in-rest )
Enhanced data-in-motion analysis (sstreaming
analytics, Hierarchy of tasks, Pub-Sub
technique)
Promote safety: (Autonomous (intra-vehicle), Cooperative
(inter-vehicle)
Sensor-rich connected vehicles: cars, buses, trucks,
rail
Social & participatory applications: active, real-time
traveler input
Cloud-based smart edge: on demand content, computing &
communications
CVST
CVST Objectives / Tasks
Novel Applications
Private Sector Using CVST Platform Autonomic Control of Transportation
Systems
Smart Transportation Management
Systems
Smart Transportation ApplicationsOntario Research Project: 2011-2015
University of Toronto & York University
Industry Partners
Multidisciplinary
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Open Flexible Platform
Leveraging Future Wireless and Cloud
Technologies
Smart Management Architecture
Improving Efficiency and Safety
Connected Vehicles & Mobile
Computing Cloud
Data Management & Event Processing
Systems
Road /
Vehicle Data
(V2R)
Vehicle to
Infrastructure
data (V2I)
Vehicle to Vehicle data
(V2V)
Personalized
Routing
Future Integrated Communication / Transportation
Datacenter
ON
U
ONU
OLT
Core /
Metro
Virtual Router Smart Edge Node
SaaS
PaaS
54-Jul-12 Connected Vehicles and Smart Transportation (CVST)
V2V V2V
V2V
V2
I
Virtual CPU / Storage
IaaS
Vehicular Network Ingredients
Network
Operator 1
Network
Operator 2Localization is
required: different
network conditions
per regionNetwork
Operator 3
Internet
Service
Operator 1
Service
Operator 2Global services are
required: region
independent
Vehicle to Roadside
Vehicle to Infrastructure
(V2I)
Wide Area (Global)
4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 6
DSRC
/
WAVE
Deployment of V2V
& V2R: requires
government
involvement
LTE3G
GSM
Ubiquitous access
required: different
types of access
networks per region
Operator 3
Vehicle to Vehicle (V2V)
Ad Hoc
Vehicle to Roadside
(V2R)
Regional Network
Data Processing Middleware in CVST
Publish/Subscribe
Paradigm
Transmitter (publish): Road sensor, GPS information, traffic lights, 0
Receiver (subscribe): Monitoring database, clients, traffic management
Broker (route): lightweight, adaptive network overlay
Publish/Subscribe
4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 7
Publish/Subscribe
Client Abstraction
Content-Based
Routing
Physical Network
(Communication &
Transportation)
Autonomic Control & Management
Decision
Execution
Managed
Monitoring
Self Optimizing
Self Organizing
Self Healing
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Managed Element
Data from vehicles fed into the autonomic loop
Data is aggregated, summarized, analyzed
Control decision, execution
Personalized routing: prime output of the control loop
Mathematical View
Network Science
•Graph theory
•Control theory
Data processing
•Streaming analytics
•Data-in-motion
•Mining
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•Mining
Previous work
•Autonomic resource management
•Virtual networks
•Network Criticality
•Clustering
•Challenges
•Large data � fast access, prediction, model building
•Aggregation in Pub/Sub system
•Stable communication overlay � cluster of vehicles
•Resilience (route optimization)
Early Work: Network Criticality in Transportation
With Increase in Traffic Demand With decrease in Traffic Supply
• Provide a robust traffic assignment in a transportation network
by minimizing Network Criticality (minNC)• Compare resulting Travel times to that of static System Optimal
Equilibrium (SOE) Optimization, which minimizes travel time (minTT)
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CVST is an ORF project
University of Toronto, York University
Industry partners
CVST project is to build an open application platform
Cloud-based
Wireless/sensor communications + mobile computing
Conclusions
Wireless/sensor communications + mobile computing
CVST promises
Build a sophisticated data processing engine
Create pervasive smart management applications
• Improve safety and efficiency of public transportation
Create novel applications
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ITS Canada Architecture (Similar to USA)
Fixed Point to Fixed Point Communications
Travelers
Remote Traveler
Support
Personal
Information
Access
Centres
Traffic
Management
Emergency
Management
Toll
Administration
Commercial Vehicle
Administration
Emergency
Management
Information
Service
Provider
Emissions
Management
Transit
Management
Fleet & Fright
Management
Archived
Data
Management
Border
Inspection
Administration
Wide Area Wireless (Mobile
Communications)
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Fixed Point to Fixed Point CommunicationsCommunications)
Ve
hic
le t
o V
eh
icle
Co
mm
un
ica
tio
ns
De
dic
ate
d S
ho
rt R
an
ge
Co
mm
un
ica
tio
ns
(DS
RC
)
Vehicle
Emergency Vehicle
Commercial Vehicle
Transit Vehicle
Maintenance & Construction Vehicle
Intermodal Freight Equipment
VehiclesRoadway
Security Monitoring
Toll collection
Parking Management
Commercial Vehicle Check
Border Inspection System
Field
ITS Trend
Past
Closed Product
Fragmented Silos
Single owner (full cost and risk)
One-size fits all
Future
Open Platform
Shared data, knowledge and resources
Co-creation of services
Customizable and Personalizable
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Context agnostic
Hard to expand
No pub-sub
Agency sources only
Limited delivery methods
Info only, no dynamic management
Context Sensitive
Extensible Large Databases
Publish-Subscribe
Multiple Info Sources
Multiple Delivery Methods/ connected cars
Incl. Dynamic management such as pricing
Network Science
• Network Science
– Newly emerging
• Social networks
• Biological networks
• Communication networks
Combination of
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– Combination of
• Graph theory
• Control theory
• Cross discipline applications
• Network Science: Communications, Computing,
& Transportation Networks
Random Walk Betweenness Centrality
• Newman
� Average number of visits to k
� When d is sink
� bsk(d) for s-d trajectories
� b total over all s-d trajectories� bk total over all s-d trajectories
� Node/link criticality
� k = bk /wk� ij = bij /wij
17
Network Criticality
� For a link (i,j)bij
wij= τ
kk Wb ×= τ2
1Local
Global� Local Part: Weight
� Global Part: Network Criticality
� Quantifies robustness
18
ττ)1(
1ˆ
−=
nn� Normalized Network Criticality
τ = τ sds,d
∑ = 2nTr(L+)
τ sd = lss+ + ldd
+ − 2lsd+
� Network criticality based on Laplacian matrix
• Path cost– Walker traverses a link (i,j)
• Incurs a cost zij.
– Average travel cost?
• Link betweenness sensitivity
Interpretations
∑∂
= ijb1τ
ˆ ϕ =1
2ˆ τ × zij
i, j
∑ wij
Fix at Budget C
• Link betweenness sensitivity
• Network criticality and congestion
• Resistance distance interpretation
• Average network utilization
∑∈ ∂−
=Eji ijwm ),(1
τ
τλ
ˆ
2≤
19
ˆ τ =1
n(n −1)τ sd
s,d
∑
V = α sdτ sd∑
Goal
It is desired to minimize network criticality
Is it possible?
Network criticality is
Monotone decreasing function of link weights
Strictly convex function of link weightsStrictly convex function of link weights
Minimization is always possible
Unique solution under appropriate constraints
Design networks for minimum criticality
20
Minimizing Network Criticality
• General Linear Case τα = α sdτ sds,d
∑
Minimize τα
Subject to zijwij( i, j )∈E
∑ = C
w ≥ 0 ∀(i, j)∈ Ewij ≥ 0 ∀(i, j)∈ E
� Condition of Optimality
21
Main Result
• Sub-Optimal Case
– Deviation from optimal solution
� Used to Derive Network Control Algorithms� Used to Derive Network Control Algorithms
� Traffic engineering
� Network planning
Controller
(Traffic Engineering)
Control
Error
Planning
Update
Parameters
(Resource)
Control Input
(Selected Path) Measured
Output
τ
τ ref
22