Connected Vehicle Applications Targeted for Environmental Improvements 2013 I 2013 ITS Cal aliforn rnia Annual nnual M Meet eeting ng San an Diego ego, C Cal alifornia Oct ctob ober 1, 1, 2013 2013 Matthew hew B Barth, h, P Profes essor or Un Univ iversity o of Ca Calif lifornia ia-Riv iversid ide Acknow owled edgem ements: UCR Res esea earch h Team eam, AERIS R Res esea earch T Team eam, M Mar arcia a Pincus us, , RITA ITA, FH FHWA WA
Approaches to Minimize Energy and Emissions Impacts of Transportation: • Build cleaner, more efficient vehicles: • make vehicles lighter (and smaller) while maintaining safety • improve powertrain efficiency • develop alternative technologies (e.g.,hybrids, fuel-cell, electric vehicles) • Develop and use alternative fuels: • Bio and synthetic fuels (cellulosic ethanol, biodiesel) • electricity • Decrease the total amount of driving: VMT reduction methods • Better land use/transportation planning • Travel demand management • Improve transportation system efficiency • Intelligent Transportation System (ITS) technologies Connected Vehicles Vehicle Automation •
Key ITS Research Areas with Energy/Emissions Impacts Advanced Vehicle Control and Safety Systems: Vehicles eliminating accidents • Longitudinal and Lateral Collision Avoidance smoother traffic flow • Intersection Collision Avoidance • Adaptive Cruise Control, Intelligent Speed Adaptation • Automated Vehicles and Roadway Systems Advanced Transportation Management Systems: Systems • Traffic Monitoring and Management eliminating congestion • Corridor Management efficient operation • Incident Management • Demand Management and Operations Advanced Transportation Information Systems: Behavior reduced driving • Route Guidance better efficiency • En-Route Driver Information travel demand mngt. Traveler Service Information connection to Transit • Electronic Payment Services variable pricing • indirect versus direct energy/emissions savings
Connected Vehicles: providing better interaction between vehicles and between vehicles and infrastructure • Safety Pilot Study • DMA (Dynamic Mobility Applications) • AERIS (Applications for the Environment and Real-Time Information Synthesis)
U.S. DOT AERIS Program: Applications for the Environment: Real- Time Information Synthesis Objectives: • Identify connected vehicle applications that could provide environmental impact reduction benefits via reduced fuel use, more efficient vehicles, and reduced emissions. • Facilitate and incentivize “green choices” by transportation service consumers (i.e., system users, system operators, policy decision makers, etc.). • Identify vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-grid (V2G) data (and other) exchanges via wireless technologies of various types. • Model and analyze connected vehicle applications to estimate the potential environmental impact reduction benefits. • Develop a prototype for one of the applications to test its efficacy and usefulness
The AERIS Approach Conduct Prototype Preliminary Cost Application Concept Benefit Analysis Exploration Develop a prototype Perform a preliminary cost for one of the Examine the State-of- benefit analysis to identify applications to test its the-Practice and high priority applications efficacy and explore ideas for AERIS and refine/refocus usefulness. Operational Scenarios research Development of Modeling and Concepts of Analysis Operations for Model, analyze, and Operational evaluate candidate Scenarios strategies, scenarios 5-year Program and applications that 3 Years into Research Identify high-level user make sense for further needs and desired development, capabilities for each AERIS evaluation and scenario in terms that all research project stakeholders can Where we are today understand
AERIS Program Status Foundational Research – Complete • Broad Agency Announcement (BAA) Projects • State-of-the-Practice Reports (applications, modeling, and eval techniques) Initial Benefit Cost Analysis – Complete • Identified key assumptions for evaluation • Benefit-cost results were used to prioritize applications for additional analysis Concept of Operations Documents – Complete • Eco-Signal Operations; Eco-Lanes; Low Emissions Zones Modeling and Evaluation – Ongoing • Eco-Signal Operations Modeling – preliminary results expected in Oct. 2013 US/EU Sustainability Working Group (SWG) – Ongoing • Developing White Papers that compare and contrast various aspects of US and EU connected vehicle research • Demonstration of a jointly developed application at the 2015 ITS World Congress in Bordeaux, France
AERIS Operational Scenarios Eco-Integrated Corridor Low Emissions Zones Management (E-ICM) Eco-Signal Operations Performance Measures Arterial Data Performance Performance Low Measures Environments Measures Emissions Zone Eco- Eco-ICM Connected Mgmt. Approach & Decision Regulatory / Eco-Driving Departure at Policy Tools Support Eco- Signalized Eco-Signal System Regulatory / Traveler Int. Eco-Traffic Regulatory / Operations Policy Tools Information Signal Policy Tools Apps Eco-Lanes Apps Priority Eco-Traffic Educational Apps Signal Tools Timing Incident Educational Connected Educational Management Tools Eco-Driving Tools Freeway Data Eco- Wireless Environments Traveler Inductive Information Low Charging Apps Corridor Emissions Zones Apps (Control) Data Environments Eco-Lanes Performance Regional (Info) Measures Data Environments Connected Eco-Lanes Eco-Traveler Information Eco-Driving Mgmt. Eco- Cooperative Eco-Speed Regulatory / Adaptive Harmonization Policy Tools Performance AFV Eco- Cruise Measures Charging/ Eco-Ramp Traveler Control Fueling Metering Information Information Apps Wireless Dynamic Educational Regulatory / Inductive Tools Eco-Routing Policy Tools Charging Dynamic Multi-Modal Multi-modal Eco-Freight Traveler Traveler Routing Information Information Dynamic Educational Eco-Transit LEGEND Tools Routing AERIS Application Connected Eco-Smart Performance Measures Eco-Driving Parking Applications Supported with AERIS Regulatory / Educational Data (R&D by Others) Policy Tool Tool
System Activities: • advanced signal control • I2V-based communications • I2V & V2I communications • network equilibration Speed DSRC Range ( r ) Vehicle 1 Vehicle 2 Vehicles Vehicle 3 2 & 3 Distance Phase 1 Phase 2 Phase 3 Phase 5 Accelerating Cruising Decelerating Accelerating Phase 4 Idling Analysis boundary
System Activities: ECO-Signal Operation vehicle trajectories distance signal n signal 4 signal 3 signal 2 signal 1 distance signal n time Time-distance diagram of signal 4 disorganized traffic through corridor signal 3 References: TARGET signal 2 fuel or M. Barth et al., “Dynamic ECO-Driving for Arterial Corridors”, emissions Proceedings of the 2011 IEEE Forum on Integrated Sustainable Transportation (FISTS) , Vienna, Austria, June, signal 1 2011. speed H. Xia et al., “Indirect Network-wide Energy/Emissions time Time-distance diagram of organized traffic Benefits from Dynamic ECO-Driving on Signalized Corridors”, Proceedings of the 2011 IEEE Intelligent Transportation through corridor using SPaT Systems Conference 2011 , Washington, DC; Oct. 2011
Eco-Approach & Departure Experiment signal controller End (-120 m) Start (+190 m) intersection 12
Human-Machine Interface Speedometer tachometer SPaT Advisory speed Vehicle location Real-time MPG Indicator Intersection location Distance to Indicator intersection 13
Eco-Approach & Departure Example Run • Cycle length of 60 sec (26 green, 4 yellow, 30 red) • The vehicle approached the intersection when the light was red. The application guided the driver to slow down early and cruise pass the intersection when the light turned green, avoiding a full stop. 14
System Activities: • intelligent speed adaptation • speed harmonization • variable Speed Limits • dynamic eco-driving • platooning • cooperative cruise control Current Speed ECO- Speed 11+ m i. over ECO-Speed 5 2 4 5 6-10 m i. over ECO-Speed 1-5 m i. over ECO-Speed At or under ECO-Speed MPH MPH
Connected Eco-Driving Experiment Energy/Emissions Non Eco-Driving Eco-Driving Difference Fuel (g) 1766 1534 -13% Vehicle automation CO2 (g) 5439 4781 -12% 97.01 50.47 -48% CO (g) could provide even 3.20 1.90 -41% HC (g) 6.28 3.97 -37% better results. NOx (g) 38.9 41.2 +6% Travel time (min) 16 Source: Barth, M. and Boriboonsomsin, K. (2009). Energy and emissions impacts of a freeway-based dynamic eco-driving system. Transportation Research Part D , 14, 400-410.
Behavior Activities: Focus on Behavior: • eco-routing • eco-driving • smart parking T vs. D T vs. D F vs. D F vs. D F vs. T F vs. T Distance (mi) Distance (mi) +30% +30% +1% +1% -23% -23% Time (min) Time (min) -14% -14% -7% -7% +8% +8% Fuel (gal) Fuel (gal) +32% +32% -2% -2% -25% -25% CO2 (kg) CO2 (kg) +31% +31% -2% -2% -25% -25% CO (g) CO (g) +180% +180% +4% +4% -63% -63% HC (g) HC (g) +85% +85% +4% +4% -44% -44% NOx (g) NOx (g) +44% +44% +1% +1% -30% -30%
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