D-STOP Symposium 2016 Closing Remarks
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Transcript of D-STOP Symposium 2016 Closing Remarks
D-STOP Symposium 2016Closing Remarks
Sanjay ShakkottaiDirector, Wireless Networking and Communications Group
1
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
2007-08
2009-10
2011-12
2013-14 $-
$2,000,000.00 $4,000,000.00 $6,000,000.00
Data Enabled Multi-scale Platform for Planning and Operations
Models and Learning Algorithms1. 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 dataServices and Applications
1. Adaptive traffic signaling2. Transit planning using network models
The Internet of Moving Things1. Vehicles as dynamic sensors2. Agile V2X communications
Image source: http://smartdesignworldwide.com ; Full link at: http://bit.ly/1MzYxMe
Data Portal & Analytics
1. Hosted at UT’S TACC2. TMC testbed3. Versioning capabilities4. Edit via free tools or ArcGIS
Public release of select data
Community
1. OpenSteetMap2. City and State
open data portalsPublished and crowd-sourced data
Transportation Agencies
Private Sector
1. App Developers2. OEMs
Agen
cy
Dat
a
Regional D
ata
Select
Access
Data Data
Open
Acce
ss
The Data Analytics Platform
Additional details: Please contact Dr. Jennifer DuthieMap image screenshot from http://www.openstreetmap.org , Transport Management Center: Source: http://www.moxa.com ; Full link at http://bit.ly/1qfBJrd
Vehicles as
Sensors
Data
The Internet of Moving Things
Sensing + Communications
Infrastructure-based sensingSensing includes radar,
LIDAR, cameras, and weather
Coordinate traffic through intersections, support
automated driving
Collect data about collisions and near-misses for
planningEffective 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
planningEffective with non-connected
cars, bicycles, and pedestrians
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 Heath
Dual low-cost (~$5) GPS/GNSSantennas mounted on vehicle
Standard GPS/GNSS positioning exhibits2-3-meter errors (actual traces)
Precise GPS/GNSS positioning exhibits2-3-centimeter errors (mockup trace—systemnot 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
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 Humphreys
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 Discovery, routing, handoff efficient
Additional details: Please contact Prof. Sriram Vishwanath
Algorithms for Large Scale Learning
Offline + Online Learning
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
45 th St
38 ½ th St
I-35
Red R
iver S
t
Airport Blvd
Guad
alupe
St
Lam
ar B
lvd
32 nd St
Manor Rd
51 st St
Northfield
Windsor Park
RidgetopHyde Park/ Northfield
Delwood II
Hancock
North University
Cherrywood/Wilshire Wood / Delwood I
MuellerBarbara Jordan Blvd
38 ½th St
Manor Rd
Other
Ramps used by neighborhood traffic, Source: Dr. J. Duthie
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
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
Applications
Real-time Operations and Long-term Planning
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 / Shakkottai
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
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 buses2. Move buses to parallel
corridor3. Transit-only lane + queue
jump and signal priority
Additional details: Please contact Dr. Jennifer Duthie
Conclusion Moving towards an integrated platform spanning sensing,
algorithms and applicationsGoals are to support both real-time operations and long-term
planning
D-STOP Center cross-cutting research spans multiple disciplinesCollaborations across disciplines to develop new methods and
algorithms