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 C H A R D F U J I M O T O A B H I L A S H A S A R O J M I C H A E L R O D G E R S
OVERVIEW 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
OVERVIEW 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
NORTH AVENUE TEST BED ‐ GREEN CORRIDOR • Utilize CoA sensor infrastructure monitor “real ‐ time” high ‐ resolution corridor level conditions • Advanced simulation corridor model capable of representing DSRC, Bluetooth, and other sensor data • Integrate signal timing and vehicle movement data to assess emissions and energy usage for simulated and field data https://cdn.pixabay.com/photo/ https://openclipart.org/image/24 2013/07/12/15/17/traffic ‐ light ‐ 00px/svg_to_png/34891/network ‐ https://cdn.pixabay.com/photo/2013/07/12/17/51/linked ‐ 149580_960_720.png wireless.png 152575_960_720.png
NORTH AVENUE TEST BED – DDDAS APPROACH DDDAS – Dynamic Data Driven Application Systems OBU WWAN* Sense: vehicle Predict: project determine current likely future position, speed, locations, energy, acc. , etc. emissions OBU OBU RSU RSU Adapt: determine DSRC*: V2R, V2I KPI, recommend RSU - Ro adside U nit driving and signal OBU – Onbo ard U nit DSRC – De dic ate d Sho rt Range Co mmunic atio n adjustment V2R – Ve hic le to Ro adside Co mmunic atio n WWAN – Wire le ss Wide Are a Ne two rk DDDAS Processing Loop * Communication between vehicle, roadside, and cloud may occur via DSRC or other WWAN application (e.g. cellular)
NORTH AVENUE TEST BED – SYSTEM ARCHITECTURE MODEL Write only Read only DB App Read Only Simulated Vehicles Read only DB App Private Private Network Network Read & Read & write DB write App Simulated Network G ‐ RTI Traveller Assistant Pre ‐ Recorded Private Private Data Network Network On ‐ server Data source Simulated World Digital World 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
NORTH AVENUE TEST BED – FIELD DEPLOYMENT SYSTEM Write only Read only DB App Internet/ Internet/ Network Network Read Read only Only DB App Vehicles G ‐ RTI Read & Read & write DB write App On ‐ server Reports & News Traveller Assistant Internet Internet Off ‐ server Cameras & Sensors Digital World Real World 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
GREEN RUNTIME INFRASTRUCTURE (GRTI) MIDDLEWARE Server Global Sync Module Network Push CppCMS (Internet/Ad ‐ Message FastCGI controller hoc) Module (mod_fcgi) Message Aggregato r Module Green Runtime Infrastructure (G ‐ RTI) middleware Distributed simulation integration framework based on DoD High Level Architecture standard (IEEE 1516) supporting DDDAS simulation, emulation, and deployment Scalable design Flexible, supporting wide variety of devices, Internet of Things Energy ‐ efficient implementation of key services Time Management (Synchronization) Data Distribution Management (Communications)
VISSIM VISSIM ‐ A microscopic, stochastic traffic simulation model that represents the real world dynamic traffic environment for freeways and streets 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 Kiel Ova, PTV, 2010 Kiel Ova, PTV, 2010
VISSIM – NETWORK TRAFFIC MODEL Link Diagram Main Interface Signal Control 3D Animation
ENERGY AND EMISSIONS MODELING MOVES ‐ MATRIX • The USEPA’s MOVES model predicts energy consumption and emissions as a function of vehicle onroad operating conditions, expressed as vehicle ‐ specific power (VSP) • The modeling approach developed by Georgia Tech yields a huge multi ‐ dimensional matrix of emission rates, from which individual vehicle and fleet emission rates can be quickly derived and applied at any modeling scale � � � � � � � � � � � � ��� � � � � � � � ∗ sin � � � 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)
ENERGY AND EMISSIONS MODELING ‐ MOVES BINNING APPROACH Emission rates established by VSP bin apply to all vehicle activity falling into each specific VSP bin 23 bins apply to each vehicle class • Deceleration/braking • Idle (0 mph) • Low ‐ speed (1 ‐ 25 mph) coast • Low speed (1 ‐ 25 mph) cruise/acceleration • Moderate speed (25 ‐ 50 mph) coast • Moderate speed (25 ‐ 50 mph) cruise/acceleration • High speed (50+ mph) cruise/acceleration
ENERGY AND EMISSIONS MODELING: ACTIVITY HISTOGRAMS X RATES FTP Driving Cycle X FTP VSP Bin Distribution Energy Consumption FTP Cycle: 63,684 kJ = 60,361 BTU 0.52 Gallons
ENERGY AND EMISSIONS MODELING: ACTIVITY BINS X RATES X FTP VSP Bin Distribution Watson Plot of the FTP Energy Consumption Driving Cycle FTP Cycle: = 63,684 kJ 60,361 BTU 0.52 Gallons
ENERGY HEAT MAP http://realtime.ce.gate ch.edu/energy_heatm ap_10/
ENERGY BUBBLE MAP http://realtime.ce.ga tech.edu/energy_bu bblemap_10/
SPECIAL EVENT MANAGEMENT Phase I – Collect baseline data Deploy advanced Georgia Tech travel monitoring app, • Deploy smartphone app to monitor game day travel congestion in collaboration with • Help City of Atlanta assess congestion advanced CoA sensors, mitigation strategies development of traffic Phase II – Design and implement mitigation systems for game incentives to change travel behavior days and special events • Develop and implement incentive partnerships • Reward participants who adopt travel behavior that reduces peak ‐ period game day congestion
SPECIAL EVENTS DOWNTOWN ATLANTA AND SPORTS ARENAS Falcons, Hawks, and Atlanta United • Mercedes ‐ Benz Stadium 71,000 ‐ seat capacity • Philips Arena 21,000 ‐ seat capacity Hotels and Georgia World Congress Center • Convention Center events Stadium/Arena/Convention Center 20,000 to 40,000 attendees • Dragon Con hosts 60,000+ attendees Centennial Olympic Park venues host millions of visitors Active business, residential, and university communities Falcons Game Day Congestion
DOWNTOWN PARKING INVENTORY 95,500 parking spaces • 148 public and 234 private lots • 47,400 public spaces • 48,100 private spaces 59% average occupancy Average $11.74/day • $2.00 to $33.00/day • High game day rates Pringle, J (2016). Parking Policies for Resurging Cities: An Atlanta Case Study. Dual Degree Master’s 1,256 downtown land acres Thesis. MCRP/MSCEE. Georgia Institute of Technology. Atlanta, GA. 364 acres of parking
CONGESTION MANAGEMENT STRATEGIES Encourage alternative modes and Monitor and identify special event shared rides congestion hot spots MARTA and GRTA Xpress bus transit Quantify time ‐ of ‐ day variability • Carpools/vanpools (public/private, Uber/Lyft, etc.) in congestion severity • Park ‐ and ‐ ride shuttle services • Assess potential benefits from: Remote park and walk options • Traffic re ‐ routing • Encourage off ‐ peak arrival and Preferential parking policies • departure Additional park ‐ and ‐ ride support • Direct vehicles onto specific routes New locations for shared ‐ ride • boarding/alighting Priority drop ‐ off/pick ‐ up locations (uncongested) • Incentivizing travel to shoulders of the peak • Priority for MARTA, vanpools, shuttles, Uber/Lyft, • etc. Quantify energy and emissions benefits from changes in travel
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