Transportation Sustainability: What can ITS Offer?

1. Transportation Sustainability: What can ITS Offer? By Hesham Rakha, Ph.D., P.Eng. Director, Center for Sustainable Mobility at the Virginia Tech Transportation Institute Professor, Charles E. Via, Jr. Dept. of Civil and Environmental Engineering at Virginia Tech

2. Presentation Outline • Eco-routing • Field and modeling results • Eco-cruise control systems • Eco-cooperative adaptive cruise control systems • On-going and future research initiatives

3. Route Choice Impacts on Fuel Consumption: Field Results

4. Objectives • Quantify the impact of route choice decisions on vehicle fuel consumption and emission levels • How?? • Second-by-second morning trip data using a portable Global Positioning System (GPS) unit at a suburb of the Washington, DC metropolitan area • Utilized MOBILE6, MOVES, VT-Micro, and CMEM models

5. Study Corridors

6. Trip Characteristics

7. Data Collection • The study used a portable GPS unit, GD30L, • Manufactured by LAIPAC Technology, Inc. • Recorded at a 1-second resolution • Probe vehicle travel data • The probe vehicle maintained the average speed of the traffic stream • Ensure that it was representative of the general flow • Collected on weekdays between March and May of 2006 using a test vehicle • The trip route (highway or arterial) was randomly selected on the day of data collection. • The size of collected data satisfy the minimum sample size (N) for a 5% significance level

8. MOBILE6 Model • Estimates emission factors based on different roadway types and average speeds • Average speed of each section (highway & arterial sections) was individually simulated and combined • Default settings used • Vehicle model year, mileage rate, vehicle age, vehicle-type percentage, and altitude information • Average speeds and road types used • The study only demonstrates the relative energy & emission differences associated with motorists’ route choices • Only exhaust running emissions • Light duty gasoline vehicle (LDGV)

9. MOVES2010 Model • Project scale was selected to estimate emissions from individual vehicle trip data. • 2010 Aug. version was used. • The following data were utilized. • Local setting: Michigan-Washtenaw County data (Fuel data and meteorology data) • Only Gasoline passenger cars • Drive schedule data (second-by-second speed profile) were imported or used – thus operational mode data were not utilized • No grade data were used to compare other emission model results • Only run exhaust emission data were used – no start emission or evaporated emissions

10. VT-Micro Model • Microscopic fuel consumption and emissions (HC, CO, NOx, CO2, PM) model using instantaneous speed and acceleration levels • Dual-regime polynomial regression model • Developed utilizing a number of data sources • Oak Ridge National Laboratory (ORNL) data (9 vehicles), EPA data(101 vehicles), and on board emission equipment data • Multiple vehicle classes including LDVs, LDTs, heavy duty truck, and bus

11. CMEM model • Developed by researchers at the University of California, Riverside • Estimates emissions as a function of the vehicle’s operating mode • Predicts second-by-second tailpipe emissions and fuel-consumption rates • CMEM vehicle categories 11 and 24 • Type11: new low-mileage vehicles • Type24: old high-mileage vehicles

12. Fuel Consumption

13. HC Emissions

14. CO Emissions

15. Contribution of High Engine Loads

16. Conclusions • This specific case study shows • If drivers select the optimum route, significant savings in fuel consumption and emissions can be achieved • Savings up to 63, 71, 45, and 20 % in HC, CO, NOx, and CO2 emissions, respectively and 23 percent in energy consumption • UE or SO traffic assignments do not necessarily minimize vehicle fuel consumption and emission levels • A small portion of the trip (10 percent, high engine load operation) may produce up to 50 percent of the total trip emissions • Significant air quality improvements and energy savings can be achieved through eco-driving

17. Network-wide Eco-Routing Impacts: The INTEGRATION Framework

18. Modeling Approach • Agent-based dynamic eco-routing tool for testing alternative routing strategies: • En-route versus pre-trip planning • Different levels of measurement accuracy • Different vehicle types and classes • Different levels of market penetration • Evaluate eco-routing strategies on large urban networks

19. Traffic and Energy Modeling • Ten different traffic assignment algorithms • Update vehicle longitudinal and lateral location (lane choice) every deci-second • Longitudinal motion based on a user-specified steady-state speed-spacing relationship & speed differential between subject and lead vehicle • Accelerations constrained by vehicle dynamics • Aerodynamic, rolling, and grade resistance forces, & driver throttle level input • Energy and emission modeling – VT-Micro

20. Eco-routing Logic • Model initialization: • Routes selected based on fuel consumption levels for travel at the facility’s free-flow speed • Vehicles report their fuel consumption experiences prior to exiting a link • Moving average fuel consumption estimate is recorded for each link for each of the five vehicle classes • Independent errors in fuel consumption estimates can also be introduced to each vehicle class using a white noise error function

21. Eco-routing Logic • Agent-based approach: • Routes updated for each vehicle at departure and prior to leaving each link • Sub-population approach: • Each vehicle class is divided into five sub-populations that receive similar routing instructions • Each driver attempts to minimize their perceived fuel consumption

22. Network-wide Testing Cleveland Network Columbus Network

23. Network Characteristics • Cleveland network • Four interstate highways (I-90, I-71, I-77, and I-490) • 65,000 vehicles in the morning peak hour. • 1,397 nodes, 2,985 links, 209 traffic signals, and 8,269 origin-destination (O-D) demand pairs using using 2010 demand data. • The Columbus network • Three interstate highways (I-70, I-71, and I-670) • Grid configuration. • Downtown area is a bottleneck during peak hours. • Network provides more opportunities for re-routing compared to the Cleveland network. • 2,056 nodes, 4,202 links, 254 traffic signals, and 21,435 O-D demand pairs.

24. Example Illustration TT Routing Eco-routing

25. Network-wide Impacts • Eco-routing consistently reduces network-wide fuel consumption levels • Reductions of 4 and 6.2 percent, respectively • 4.8 and 3.2 percent increase in the average travel time

26. Network-wide Impacts • Results consistent for different vehicle types ORNL Vehicle Fuel Efficient Vehicle

27. Network-wide Impacts

28. Eco-Cruise Control Systems

29. VT-CPFM Model • Virginia Tech Comprehensive Power-based Fuel consumption Model (VT-CPFM) • Has the ability to produce a control system that does not result in bang-bang control and • Is easily calibrated using publicly available data without the need to gather detailed engine and fuel consumption data. • Estimates CO2 emissions (R2=95%) Where: α0, α1, α2 and β0, β1, and β2 are model constants that require calibration, P(t) is the instantaneous total power in kilowatts (kW) at instant t, and w(t) is the engine speed at instant t.

30. Manual vs. Cruise Control Driving

31. Eco-Cruise Control (ECC) • The research team has developed a predictive eco-cruise control system

32. Eco-Cruise Control (ECC) • Optimization logic • Dynamic programming • Dijkstra’s shortest path algorithm or • Cost function Where, w1 is the weight factor for the fuel consumption level, w2 is the weight factor for deviation from the target speed, w3 is the weight factor for gear changes, v0 is the initial speed, v1 is the final speed, vref is the target speed, g0 is the initial gear, g1 is the final gear, FC(v0,v1) is the fuel consumption from v0 to v1 over a stage, FC(vref) is the fuel consumption at vref over a stage.

33. NYC to LA Simulation • 2790 miles with mostly highway sections • Use I-80, I-76, I-70, I-15, and I-10 route • Assumed no interaction with other vehicles

34. NYC to LA Simulation

35. Eco-Cruise Control in the Vicinity of Signalized Intersections

36. SPAT Data and Smart Traffic Signals • Scenario 1: TTI @ current speed falls in green indication. • Scenario 2: Vehicle can accelerate to speed-limit and then TTI falls in green indication. • Scenario 3: TTI @ current speed/speed-limit is far from next green indication. • Scenario 4: Inducing a delay in trajectory can allow the vehicle to proceed without fully losing its inertia.

37. Fuel Savings

38. On-going and Future Work • Eco-traffic signal control: • Designing of traffic signal timings to reduce vehicle fuel consumption levels • Developing real-time transit vehicle routing and scheduling procedures to minimize fleet fuel consumption levels • Developing eco-drive systems: • Integrating the proposed predictive ECC system within car-following models

39. Publications • Rakha, H., Ahn, K., Moran, K., Saerens, B., and Van den Bulck, E. (2011), " Virginia Tech Comprehensive Power-based Fuel Consumption Model: Model Development and Testing," Transportation Research Part D: Transport and Environment. doi:10.1016/j.trd.2011.05.008. • Rakha, H., Ahn, K., Faris, W., and Moran, K. (2010), “Simple Vehicle Powertrain Model for Use in Traffic Simulation Software,” 89th Transportation Research Board Annual Meeting, Jan. 10-14, Washington D.C. (Paper 10-0201). • Ahn, K., Rakha, H., and Moran, K. (2011), "ECO-Cruise Control: Feasibility and Initial Testing," 90th Transportation Research Board Annual Meeting, Jan. 24-27, Washington D.C. (Paper 11-1031). • Rakha, H., Ahn, K., Moran, K., Saerens, B., and Van den Bulck, E. (2011), "Simple Comprehensive Fuel Consumption and CO2 Emission Model based on Instantaneous Vehicle Power," 90th Transportation Research Board Annual Meeting, Jan. 24-27, Washington D.C. (Paper 11-1009). • Rakha, H., Ahn, K., and Moran, K. (2011), "INTEGRATION Framework for Modeling Eco-routing Strategies: Logic and Preliminary Results," 90th Transportation Research Board Annual Meeting, Jan. 24-27, Washington D.C. (Paper 11-3350). • Park S., Rakha H., Ahn S., and Moran K. (2011), “Predictive Eco-Cruise Control: Algorithm and Potential Benefits,” 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, Vienna, Austria, June 29 - July 1, 2011. • Park S., Rakha H., Ahn K., and Moran K. (2012), "A Study of Potential Benefits of Predictive Eco-Cruise Control Systems," Transportation Research Board 91st Annual Meeting, Washington DC, January 22-26, CD-ROM [Paper # 12-0795]. • Ahn K., Rakha H., and Moran K. (2012), "System-wide Impacts of Eco-routing Strategies on Large-scale Networks", Transportation Research Board 91st Annual Meeting, Washington DC, January 22-26, CD-ROM [Paper # 12-1638]. • Park S., Rakha H., Ahn K., and Moran K. (2012), "Predictive Eco-cruise Control System: Model Logic and Preliminary Testing," Transportation Research Board 91st Annual Meeting, Washington DC, January 22-26, CD-ROM [Paper # 12-0794].

40. Thank You! ??