Yinhai Wang - Smart Transportation: Research under Smart Cities Context - GCS16

Challenges and Opportunities in
Smart Transportation Research
under the Smart Cities Context
Yinhai Wang, PhD
University of Washington
IEEE Smart Cities Steering Committee
Email: yinhai@uw.edu Tel: 1-206-616-2696
For the 2016 Gigabit City Summit
May 17, 2016

PacTransSTARLabResearchonBigDataandE-Science
1
Background
Image source: https://nocturnalinexistencia.files.wordpress.com/2013/05/hacinamiento-1.jpg

IBM Smarter Cities
2
Image source: IBM Smarter Cities at http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/

IEEE Smart Cities Initiative
3
 The IEEE Smart Cities Initiative
seeks to apply technology to
improve cities and the quality of
life for their residents.
 It is a process of improving urban
living by implementing
technologies throughout a city’s
infrastructure for efficiency,
sustainability, and convenience.
 Smart city is a way of being and a
goal for future development

Hot Topics Discussed at ISC2
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2015 IEEE Smart Cities Conference: Guadalajara, Mexico, Oct. 25-28, 2015
http://sites.ieee.org/isc2/

Come to Join ISC2-2016!
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2016 IEEE Smart Cities Conference: Trento, Italy, Sept. 12-15, 2016
http://sites.ieee.org/isc2/

Hot Problems Worldwide
Transportation Environment
Energy
• Fuel-driven vehicles are
responsible for 90 percent
of the risk of developing
cancer in LA.
• Mobile sources are
responsible for a third
of local PM2.5
emissions.
• Traffic crashes kills
1.26 million people each
year.
Transportation is a big deal!!!

Key Transportation Issues
 Transportation Safety
 Transportation efficiency
 Convenience and Sustainability
These align well with goals of Smart Cities initiatives.
Is transportation a low
hanging fruit on the
smart cities tree?

Transportation Innovation Examples
Image sources: http://i2.cdn.turner.com/money/dam/assets/140606132555-uber-billion-620xa.jpg
http://www.sharingcodes.com/wp-content/uploads/2014/07/car2go-promo-codes.jpg

Sources of Intelligence
How can we do better?
 All these applications need intelligence
 Intelligence comes from data analytics
 Smart cities and IOT offer new and highly valuable
data sources
 Theoretical framework for data integration is a
must to make a proper use of all available data
valuable for transportation
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Transportation Big Data is a Key
 Designing and deploying sensor networks for monitoring the
city’s condition
 Building a big data stream through properly collecting and
integrating datasets
 Mining big data to feel the social “mood”
 Supporting informed decisions by combining the future goal,
current condition, and available resources
10

Age of Big Data
11Image sources: http://connectedvehicle.challengepost.com/submissions/2912-dsrc-the-roadway-to-intelligent-transportation

Challenges
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Traffic
Sensors
Transportation Big Data!

Opportunities with Big Data
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Many key questions can be answered by using big data:
 How to quantify the benefit from a transportation investment?
 What is the impact of a road construction project on travel?
 Which infrastructure design is greener consider life-cycle
operational impacts?
 Where do pedestrians go and how to improve their safety?
 How to improve transit services without adding new
resources?
 Where do trucks go and how to guide them to the best routes?
 …

Challenges to Transportation Professionals
 Extract Values from Big Data Streams
 High volume and high velocity
 High variety and high variability
 Methods and tools to take values out
 Manage Big Data
 Spatial and temporal features
 Storage and query efficiencies
 Privacy protection
 Understand and Use Big Data
 Problems with classical transportation theory
 Weak data training in transportation curriculum
 Isolated to systematic view
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Research/Innovation Needs
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Proposed actions to address the gaps:
 Actively pull what we need from the existing data resources
 Build our own stream of big data
 Design a standard mechanism for connecting transportation
related datasets
 Develop e-science transportation methods to take advantage
of the spatial and temporal datasets to support transportation
analysis and decision making
 Build big data analytics tools to facilitate usage of big data
 Develop new courses in transportation curriculum to make
our students/working professionals ready for the big data era

Research/Innovation Needs
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What is e-science?
 Computationally intensive science that is carried out in
highly distributed network environments
 Application of computer technology to the undertaking of
modern scientific investigation
E-science of transportation:
 Computationally intensive science for scientific
investigations in transportation issues using immense data
sets

Research Examples
 Extracting transportation data from big data sources
 Digital Roadway Interactive Visualization and
Evaluation Network (DRIVE Net)
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Data Extraction from Mobile Networks
 60% of the world population has a cell phone
 91% of Americans
 257 million data-capable devices in US (50 mil
smartphones)
 90% of Chinese in countryside areas
 Devices as data sources
 Count devices, not cars
 Devices as sensors
 Almost no maintenance
 Automatically scaling
 Ability to relay data back
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KNOWLEDGE
++

Mobile Sensing for Pedestrian Data
 Smartphone App (Mobile Monitor)
 Scans the Bluetooth spectrum
 Writes down GPS coordinates and MACs seen
*phones used in testing courtesy of Dr. Borning
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Verification by Simulation
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Big Data Analytics: DRIVE Net
 DRIVE Net is a system for the data sharing,
visualization, modeling, and analysis
 Web-based system for e-Science investigations
 Real-time traffic visualization
 Data sharing platform
 Statistical Modeling
 Vehicle emissions quantification
 Mobile Sensing
 Freeway Performance Measurement
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DRIVE Net Architecture
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City of Bellevue WSDOT WITS and HSIS
STAR Lab
FTP Server
FTP
FTP
GPS Trucking Vandors
Manual
Data
Exchange
Traffic
Database Server
Internal
Connection
Databases
VPN
SQ
L/JD
BC
GSM/GPRS
Databases
Excel files
Data Warehouse
Bluetooth Device
City of Lynnwood
Firewall
Traffic
Database Server
Internal
Connection
STAR Lab
Satellite Server
ODBC/
SQL
DRIVE Net Server
Traffic Practitioners
HTML
Researchers
HTML
Travelers
HTML

DRIVE Net: LOS Map
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DRIVE Net: Congestion Report

DRIVE Net: Emission
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PacTransSTARLabResearchonBigDataandE-Science DRIVE Net: Safety Performance
Measurement
27

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DRIVE Net: Travel Time Reliability Analysis

 Autonomous vehicles will change car ownerships
 Parking facilities will be significantly reduced
 Traffic network utilization will be optimized
 Traffic congestion will be mitigated or even disappear
 Cars will be seamlessly connected
 Virtual infrastructure for transportation will appear
Vision of Future Smart Transportation

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Thanks for your attention!
 Acknowledgment
Thanks to the UW STAR Lab research team.
The presented projects were partly funded by
Washington State Department of Transportation
(WSDOT), PacTrans, and China Natural Science
Foundation. We appreciate their funding support!

Yinhai Wang - Smart Transportation: Research under Smart Cities Context - GCS16

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Yinhai Wang - Smart Transportation: Research under Smart Cities Context - GCS16