2016 D-STOP Symposium ("Smart Cities") session by WNCG's Sanjay Shakkottai. Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/

1. D-STOP Symposium 2016
Closing Remarks
Sanjay Shakkottai
Director, Wireless Networking and Communications
Group
1

2. Wireless Networking and Communications Group
125 Grad Students
Affiliates champion large
federal proposals, provide
technical input/feedback,
research support
WNCG provides pre-competitive
research, technical expertise,
first access to students
Significant number of students
intern/work full-time for affiliates
Affiliates provide
real world context
Industrial Affiliates22 Faculty
$-
$2,000,000.00
$4,000,000.00
$6,000,000.00

3. Data Enabled Multi-scale Platform for Planning and Operations
Models and Learning Algorithms
1. New regression algorithms for noisy
and high-dimensional data
(leverage vast offline data)
2. Online learning for real-time
decisions (leverage real-time data)
3. Models for high dimensional data
Services and Applications
1. Adaptive traffic signaling
2. Transit planning using network models
The Internet of Moving Things
1. Vehicles as dynamic sensors
2. Agile V2X communications
Image source: http://smartdesignworldwide.com ; Full link at: http://bit.ly/1MzYxMe

4. Data Portal & Analytics
1. Hosted at UT’S TACC
2. TMC testbed
3. Versioning capabilities
4. Edit via free tools or ArcGIS
Public release of select data
Community
1. OpenSteetMap
2. City and State open
data portals
Published and crowd-sourced data
Transportation
Agencies
Private Sector
1. App Developers
2. OEMs
AgencyData
Regional
Data
The Data Analytics
Platform
Additional details: Please contact Dr. Jennifer Duth
Map image screenshot from http://www.openstreetmap.org , Transport Management Center: Source: http://www.moxa.com ; Full link at http://bit.ly/1qfBJrd

5. The Internet of Moving Things
Sensing + Communications

6. Infrastructure-based sensing
Sensing includes radar, LIDAR,
cameras, and weather
Coordinate traffic through
intersections, support automated
driving
Collect data about collisions
and near-misses for planning
Effective with non-connected cars,
bicycles, and pedestrians
Sensing includes radar, LIDAR,
cameras, and weather
Coordinate traffic through
intersections, support automated
driving
Collect data about collisions
and near-misses for planning
Effective with non-connected cars,
bicycles, and pedestrians

7. Radar-aided millimeter wave communication
mmWave BS
supporting
V2X+radar
antennas
Radar beam
Millimeter wave is used for both radar sensing and high bandwidth
communication
communication beams
Radar can be used to configure
communication link more efficiently
Additional details: Please contact Prof. Robert Hea

8. Dual low-cost (~$5) GPS/GNSS
antennas mounted on vehicle
Standard GPS/GNSS
positioning exhibits
2-3-meter errors
(actual traces)
Precise GPS/GNSS
positioning exhibits
2-3-centimeter errors
(mockup trace—system
not yet operational)
Carrier-phase-based processing of GPS/GNSS signals enables 100x
improvement in accuracy compared to standard GPS/GNSS
positioning. But current costs are too high (~$2k). WNCG researchers
are developing a ~$50 sensor that achieves reliable instantaneous
precise positioning.
Applications
Lane violation statistics: Where are
drivers routinely departing the lane?
Lane-responsive signaling
Intuitive heads-up-display: Driver sees
path to destination “painted” on
roadway
“Last moment” lane keeping: Vehicle
nudges car back into lane only when
unintentional lane departure is
imminent. Unlike Tesla’s Autopilot, this
keeps driver engaged

9. Densely-space reference stations compensate for GPS/GNSS signal atmospheric delays so that
vehicles can be instantaneously positioned to sub-decimeter globally-referenced accuracy. UT-
Samsung centimeter-accurate mobile positioning system (CAMPS) reference network in Austin,
Texas, with site hosting courtesy of TxDOT.
Additional details: Please contact Prof. Todd Humphr

10. Communications – The Internet of Moving Things (IoMT)
 Full Duplex Radios - Can transmit &
receive at the same frequency at
same time
 Thought to be impossible 8 years
ago
 Possible through self-interference
isolation and cancellation
 110+dB of isolation/cancellation
necessary
 Enables listen-while-talking
 Much more efficient mobile
meshing
 Full duplex + mobile meshing
 Low overhead, high throughput
meshing
 Connect people and vehicles as they
move Additional details: Please contact Prof. Sriram Vishwan

11. Algorithms for Large Scale Learning
Offline + Online Learning

12. Mixtures and Non-Linearities in Large Scale Data Analysis
 Linear, Logistic and Non-linear regression are fundamental for prediction and
planning
 Examples: transit time vs. daily flows, flow vs. speed, responses to network stressors
or diversions or to future demand and flow patterns
 Mixtures: Populations are mixed, and may require simultaneous clustering and
regression/classification, when clustering-as-data-preprocessing is impossible
 Nonlinearities: Discover structure without expensive/intractable non-parametric
models
New algorithms for:
1. Solving the simultaneous clustering-regression
problem (tensor methods)
2. Structure recovery through unknown non-linear
transforms (second-moment methods)
Additional details: Please contact Prof. Constantine Caramanis
Northfield
Windsor Park
RidgetopHyde Park/
Northfield
Delwood II
Hancock
North University
Cherrywood/WilshireWood / Delwood I
Mueller
Barbara Jordan Blvd
38 ½th St
Manor Rd
Other
Ramps used by neighborhood traffic, Source: Dr. J. Duthie

13. Online Decision Making – Bandit Algorithms
 Online learning algorithms for real-time matching between servers and
demand
 Freight: Servers == trucks; Demand == packages/cargo
 Travel: Servers == cars; Demand == passengers
 Server availability and demand varies with time
 Service time is random
 Market matching only if servers and demand available New algorithms based on
queueing bandits for online
learning and resource allocation
Additional details: Please contact Prof. Sanjay Shakkottai
Source: http://volvotrucks.com ; Full link at: http://bit.ly/1WWobLD

14. Modeling High Dimensional Heterogeneous Data
New Spatial Generalized Heterogeneous Data Model
Correlation across various dimensions (of the
dependent variables) are captured using latent
constructs
Maximum Approximate Composite Marginal
Likelihood (MACML) estimation approach is used for
estimation of GHDM
Source: http://www.networkworld.com ; Full link at: http://bit.ly/235TMOy
Additional details: Please contact Prof. Chandra Bhat
 Multimodal data: conventional sources + cameras, GPS, cell phone
tracking
 Methodologies to combine and aggregate high dimensional heterogeneous data

15. Applications
Real-time Operations and Long-term Planning

16.  Pre-timed: Static signal timings cannot respond to real-time conditions
Create progression and synchronize operation for maximum flow under a
deterministic load
 Actuation: Intersections use sensors to detect waiting vehicles and
adjust signal timings responsively
Can respond to current demand, but lose benefits of coordination
Both paradigms have significant
drawbacks that can be
overcome with new technology
Source: http://www.moxa.com ; Full link at http://bit.ly/1qfBJrd
Real-time Data-driven Signal Timing
Additional details: Please contact Profs. Boyles / Shakko

17.  New algorithms seek the best of both paradigms: adaptive control with
global optimality properties
 Sensors can measure queue lengths and estimate turn fractions and
(approximate) destinations
 Analogous to packet routing problems in telecommunication networks,
where fast, decentralized algorithms exist
Source: http://www.moxa.com ; Full link at http://bit.ly/1qfBJrd
Application: Data-driven Signal Timing
Source: US DOT ; Full link at http://1.usa.gov/1pIKhGv

18. Bus Transit Planning along the Guadalupe Corridor
Estimates of
Guadalupe corridor
boardings and
alightings
Data-driven modeling and
planning to study various
“what-if” scenarios:
1. Dedicate one lane to buses
2. Move buses to parallel corridor
3. Transit-only lane + queue jump
and signal priority
Additional details: Please contact Dr. Jennifer Duthie

19. Conclusion
 Moving towards an integrated platform spanning sensing,
algorithms and applications
 Goals are to support both real-time operations and long-term planning
 D-STOP Center cross-cutting research spans multiple disciplines
 Collaborations across disciplines to develop new methods and
algorithms