ATLANTA SMART CITY INITIATIVE CORRIDOR & SPECIAL EVENT - - PowerPoint PPT Presentation
ATLANTA SMART CITY INITIATIVE CORRIDOR & SPECIAL EVENT APPLICATION FA C U LT Y S T U D E N T S M I C H A E L H U N T E R A R A D H YA B I S W A S R A N D A L L G U E N S L E R J O H N B O L E N A N G S H U M A N G U I N S O M D U T R O Y R I
FA C U LT Y
M I C H A E L H U N T E R R A N D A L L G U E N S L E R A N G S H U M A N G U I N R I C H A R D F U J I M O T O M I C H A E L R O D G E R S
S T U D E N T S
A R A D H YA B I S W A S J O H N B O L E N S O M D U T R O Y A B H I L A S H A S A R O J
Demonstrate Intelligent Transportation System (ITS) technologies and congestion mitigation in Atlanta
North Avenue testbed ‐ Green Corridor Capitalized on deployed vehicle‐to‐ vehicle (V2V) and vehicle‐to‐roadside (V2R) technologies in the active testbed to advance a “Green Corridor”
http://www.arch2o.com/wp‐content/uploads/2015/12/Arch2O‐ Connected‐vehicles‐04.jpg
Demonstrate Intelligent Transportation System (ITS) technologies and congestion mitigation in Atlanta.
Special Events Management Deploy advanced Georgia Tech travel monitoring app, in collaboration with advanced CoA sensors, monitor congestion, and develop traffic mitigation systems for game days and special events
http://www.publicdomainpictures.net/pictures/210000/v elka/people‐cheering.jpg
resolution corridor level conditions
DSRC, Bluetooth, and other sensor data
emissions and energy usage for simulated and field data
https://cdn.pixabay.com/photo/ 2013/07/12/15/17/traffic‐light‐ 149580_960_720.png https://openclipart.org/image/24 00px/svg_to_png/34891/network‐ wireless.png https://cdn.pixabay.com/photo/2013/07/12/17/51/linked‐ 152575_960_720.png
OBU RSU DSRC*: V2R, V2I RSU
nit OBU – Onbo ard U nit DSRC – De dic ate d Sho rt Range Co mmunic atio n V2R – Ve hic le to Ro adside Co mmunic atio n WWAN – Wire le ss Wide Are a Ne two rk RSU OBU * Communication between vehicle, roadside, and cloud may
application (e.g. cellular) WWAN* OBU
Sense: vehicle determine current position, speed, acc., etc. Adapt: determine KPI, recommend driving and signal adjustment Predict: project likely future locations, energy, emissions DDDAS Processing Loop
DDDAS – Dynamic Data Driven Application Systems
G‐RTI
On‐server
Read Only DB Read only App
Write only DB
Digital World Simulated World
Simulated Network Data source Simulated Vehicles
Read & write DB Read & write App Read only App
Private Network Private Network
Traveller Assistant
Pre‐Recorded Data
Private Network Private Network
Images: NS3: http://personal.ee.surrey.ac.uk/Personal/K.Katsaros/images/logos/ns3logo.png vissim: http://vision‐traffic.ptvgroup.com/fileadmin/_processed_/csm_Screenshot_PTVVissim_Multimodal_Systems_Scooter_dd0fabddfc.gif Laptop: https://images.vexels.com/media/users/3/136276/isolated/lists/31d41117ba74dd2475728b29db3ef718‐laptop‐flat‐icon.png
G‐RTI
On‐server
Read Only DB Read only App
Write only DB
Off‐server
Traveller Assistant
Digital World Real World
Reports & News Cameras & Sensors Vehicles
Read & write DB Read & write App Read only App
Internet Internet Internet/ Network Internet/ Network
Images Cars: http://www.goauto.com.au/mellor/mellor.nsf/story2/07EC51D606FC8C86CA25791A0006D3CA/$file/GM_tech_large.jpg?OpenElement Weather :http://www.americas‐best.com/graphics/pics_hot‐weather‐forecast.gif CCTV camera: http://www.clker.com/cliparts/1/U/3/F/h/V/surveillance‐camera‐md.png
Network (Internet/Ad‐ hoc) FastCGI (mod_fcgi)
Server
Global Sync Module Push Message Module Message Aggregato r Module
CppCMS
controller
Green Runtime Infrastructure (G‐RTI) middleware
(IEEE 1516) supporting DDDAS simulation, emulation, and deployment
VISSIM ‐ A microscopic, stochastic traffic simulation model that represents the real world dynamic traffic environment for freeways and streets
Kiel Ova, PTV, 2010 Kiel Ova, PTV, 2010
Models individual vehicle behavior, various traffic control devices, intersections and interchanges, dynamic demands, flexible network layouts, roadway geometry, merging, vehicle routing, etc. Utilizes Psycho‐physical car following model (Prof. Wiedemann, 1974 and 1999)
Kiel Ova, PTV, 2010
Main Interface Link Diagram Signal Control 3D Animation
ENERGY AND EMISSIONS MODELING MOVES‐MATRIX
function of vehicle onroad operating conditions, expressed as vehicle‐specific power (VSP)
dimensional matrix of emission rates, from which individual vehicle and fleet emission rates can be quickly derived and applied at any modeling scale
VSP = Vehicle Specific Power (KW/metric tonne) M = Fixed mass factor for the sourceType (tonnes) m = Source mass (tonnes) A = Rolling resistance (kW/meter/second) B = Rotational resistance (kW‐sec2/meter2) C = Drag coefficient kW‐second3/meter3 v = Vehicle velocity (meters/sec) a = Vehicle acceleration (meters/second2) g = Gravitational acceleration (9.8 m/second2) Ɵ = Road grade angle (radians or degrees, as needed)
Emission rates established by VSP bin apply to all vehicle activity falling into each specific VSP bin 23 bins apply to each vehicle class
cruise/acceleration
FTP VSP Bin Distribution
FTP Cycle: 63,684 kJ 60,361 BTU 0.52 Gallons
Energy Consumption
FTP Driving Cycle
Watson Plot of the FTP Driving Cycle FTP VSP Bin Distribution
FTP Cycle: 63,684 kJ 60,361 BTU 0.52 Gallons
Energy Consumption
http://realtime.ce.gate ch.edu/energy_heatm ap_10/
http://realtime.ce.ga tech.edu/energy_bu bblemap_10/
Deploy advanced Georgia Tech travel monitoring app, in collaboration with advanced CoA sensors, development of traffic mitigation systems for game days and special events
Phase I – Collect baseline data
game day travel congestion
mitigation strategies
Phase II – Design and implement incentives to change travel behavior
partnerships
behavior that reduces peak‐period game day congestion
SPECIAL EVENTS DOWNTOWN ATLANTA AND SPORTS ARENAS
Falcons, Hawks, and Atlanta United
71,000‐seat capacity
21,000‐seat capacity Hotels and Georgia World Congress Center
20,000 to 40,000 attendees
Centennial Olympic Park venues host millions
Active business, residential, and university communities Stadium/Arena/Convention Center Falcons Game Day Congestion
95,500 parking spaces
59% average occupancy Average $11.74/day
Pringle, J (2016). Parking Policies for Resurging Cities: An Atlanta Case Study. Dual Degree Master’s
1,256 downtown land acres 364 acres of parking
Encourage alternative modes and shared rides
Encourage off‐peak arrival and departure Direct vehicles onto specific routes
etc.
Monitor and identify special event congestion hot spots Quantify time‐of‐day variability in congestion severity Assess potential benefits from:
boarding/alighting
Quantify energy and emissions benefits from changes in travel
GAMESCOUT PROJECT PHASING 21
GAMESCOUT OUTREACH AND RECRUITMENT PHASE I DEPLOYMENT
Hel Help Geor Georgi gia Te Tech and and the the Ci City ty of
Atlanta mo moni nitor tor and and manag manage ga game da day co congestion
Download Download the the Gam GameSc Scout App App fo for yo your Andr Android pho phone ne via via the the Goo Google le Pl Play St Stor
google.com).
Partic rticip ipants ts re receive cl club ub disc discounts at at the the ga game!
Please feel free to contact me at michael.hunter@ce.gatech.edu