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

4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 4

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

4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 8

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

4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 9

•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)

4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 10

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

4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 11

Q&AQ&A

4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 12

Backup SlidesBackup Slides

4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 13

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)

4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 14

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

4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 15

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

4-Jul-12 Connected Vehicles and Smart Transportation (CVST) 16

– 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