Final Report: Framework for Context-Sensitive Spatially- and Temporally-Resolved Onroad Mobile Source Emission Inventories

EPA Grant Number: R834550
Title: Framework for Context-Sensitive Spatially- and Temporally-Resolved Onroad Mobile Source Emission Inventories
Investigators: Frey, H. Christopher , Rouphail, Nagui , Xuesong, Zhou
Institution: University of North Carolina , University of Utah
EPA Project Officer: Chung, Serena
Project Period: May 16, 2010 through May 15, 2013 (Extended to May 15, 2014)
Project Amount: $500,000
RFA: Novel Approaches to Improving Air Pollution Emissions Information (2009) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Air

Objective:

The objectives of this research are to: (1) develop a robust, multi-scale methodology for estimating emission inventories (EIs) at various spatial and temporal scales; (2) evaluate the utility of meso-scopic and microscopic transportation models to predict vehicle activity at sufficient resolution and in analyzing future vehicle control or network design scenarios; and (3) quantify the relative contribution of vehicle type, vehicle activity and traffic control measures on the magnitude and variability in regional emission estimates.

Summary/Accomplishments (Outputs/Outcomes):

We created a database of legacy data (approximately 70 vehicles) and new data (approximately 30 vehicles) collected in this project (for a total of 100 vehicles).  We developed a rigorous standard framework for measurement, quality assurance, analysis, reporting, and archival of data, which has been applied to all vehicles measured on our standard study routes in the Raleigh and Research Triangle Park, NC, areas.  These data enable us to quantify vehicle activity, energy use, and emissions for a wide variety of vehicles by model year, mileage accumulation, engine size, and body style.

We developed a study design for field data collection of vehicle activity, energy use, and emissions to complete the database described above. The study design accounts for infrastructure data, vehicle technology and fuels, vehicle dynamics, and ambient conditions. These data were obtained using multiple instruments deployed simultaneously for on-board data collection. A Global Positioning System (GPS) receiver with barometric altimeter was used for measuring vehicle location and elevation on a second-by-second basis. The elevation data enables quantification of road grade, which in turn is an input to estimation of Vehicle Specific Power (VSP). An engine control unit (ECU) data logger was used to record data via the On-Board Diagnostic (OBD) interface, which is standard on all 1996 and newer light duty vehicles in the United States. These data are used to estimate air and fuel flow for the engine. A Portable Emissions Measurement System (PEMS) was used that measures the tailpipe exhaust concentration of NO, HC, CO, and CO2.

There were 30 vehicles involved in data collection. The sample includes a wide range of chassis type, model year, engine size, and accumulated mileage. Each vehicle was driven approximately 115 miles on predetermined routes. There typically are 4 hours or more of second-by-second data for each vehicle. The final results for each vehicle are the average emission rates of CO2, NOx, CO, and HC for each of 14 VSP bins for every vehicle. The fuel use and emission rates are closely related to VSP. The modal emission rates of other pollutants also increase monotonically with positive VSP. There is substantial variability in fuel use and emission rates with engine load.

We compared emission factors estimated with the U.S. EPA’s Motor Vehicle Emissions Simulator (MOVES) to measured emission factors based on data for 100 vehicles for six driving cycles per vehicle, for a total sample of approximately 600 vehicle-cycle combinations. The vehicles range from 1996 to 2013 model year, with engine sizes of 1.5 to more than 5 liters, and accumulated mileage from approximately 3,000 to 230,000 miles. There are 67 passenger cars and 33 passenger trucks included in the measurement with ages from 0 to 14 years. There are more than 4 hours of continuous driving on six driving cycles for each measured vehicle. Therefore, there are approximately 1.4×106 seconds of 1 Hz driving cycle and emission rate. For each of 97 vehicles, data are available for six driving cycles. For each of three vehicles, data are available for only three driving cycles. Therefore, there are a total of 591 cycle average emission rates estimated for each of CO2, NOx, CO, and HC for both empirical data and MOVES estimates. Overall, the trends in MOVES estimated emission factors with respect to driving cycles, vehicle type, regulatory tier, and related factors such as cycle average speed, road type, vehicle age, and mileage accumulation are qualitatively consistent with those observed in independent empirical data. The magnitude of CO2 cycle average emission rates is similar for MOVES and empirical data. The magnitude of cycle average emission rates for NOx, CO, and HC is typically higher for MOVES than for empirical data, which is attributed to the reported incorporation of high emitting vehicles into the MOVES default OpMode rate databases.

Conclusions:

A simplified (or reduced form) version of MOVES was developed and shown to be highly precise in producing the same cycle average emission rates for a wide variety of vehicle types, vehicle ages, pollutants, and driving cycles. The simplified model takes into account many factors that are constant over a multi-hour traffic simulation based on calibration to a base emission rate obtained via one run of MOVES. Further, the simplified model accounts for variability in cycle average emissions associated with variability in vehicle dynamics based on the same VSP and speed based OpMode bins that are the foundation of MOVES. The simplified model is computationally more simple than MOVES and can be easily encoded as part of a travel demand model (TDM) or traffic simulation model (TSM). For example, the simplified model can be used to estimate emissions for link-based, trip-based, or area-wide driving cycles for simulated vehicles. The novelty of this work is that no one else has taken this approach to developing a simplified alternative to the MOVES model that can be incorporated into a TSM or used for iteration case studies such as to evaluate many driving cycles. The simplified model takes into account vehicle types that comprise 95% of the onroad fleet. The model can be expanded, if needed, to include other vehicle types. The simplified model is shown to accurately predict differences in cycle average emission rates for cycles with similar average speeds but significant differences in the distribution of engine load. The model also is demonstrated based on applications to empirically observed driving cycles, indicating the flexibility to represent a variety of such cycles.

MOVES Lite was incorporated into DTAlite, a dynamic traffic assignment traffic simulation model. DTAlite simulates the speed trajectories of individual vehicles operating on each link of an urban scale road network. The baseline DTALite model for the Research Triangle region was calibrated with sensor based volume information provided by Traffic.com on the major freeways and arterials for the modeled time period. Calibration was done at a 15-minute resolution. The calibrated network was able to match the baseline traffic information with a R2 value of 0.82.

Four types of traffic management strategies (TMS) were modeled to compare relative improvements: (1) demand-side Mode Shift (MS); (2) regulatory intervention Fleet Replacement (FR); (3) operational modification Peak Spreading (PS); and (4) response to Incidents (INC) based on information provided through Variable Message Sign (VMS). Case studies were formulated using a large-scale real world network for the Research Triangle Park, NC, region. MS depends on policy decision on percentage of vehicle-trips to be reduced. The FR scenario results in changing age distribution of different vehicle classes considered in modeling. There are two vehicle classes considered under the demand classes: passenger car (PC) and passenger truck (PT).  FR as a regulatory decision can be used to change the age distribution of these vehicle classes and then the model is run to equilibrium for estimation of emissions. The PS scenario is modeled as shifted departure time dynamic demand. The operational and information improvements from peak-spreading strategies will result in reduction of peakedness in demand profile and more uniform distribution of demand over the time period. The hourly demand tables are adjusted to match a desired level of peak spreading. The INC scenario can be modeled in the simulation process on a link-basis by specifying day, event start time, event end time, and percentage of capacity reduction.

We have developed an integrated computationally efficient framework for simulation of traffic activity, energy use, and emissions that is applicable to urban regions. This framework includes an open-source dynamic traffic assignment model, DTAlite, that has been coupled with a newly developed simplified representation of the U.S. EPA’s MOVES vehicle emission factor model. We refer to the latter as “MOVES Lite.” DTAlite simulates individual vehicle movements from origin to destination along selected paths in an urban scale transportation network, and predicts the second-by-second speed and acceleration of each vehicle. MOVES Lite enables prediction of the energy use and emission rates of vehicles based on vehicle type and vehicle age, and currently is calibrated to represent five vehicle types (passenger cars, passenger trucks, light commercial trucks, short-haul trucks, and long-haul trucks) for vehicles of ages 0 to 30 years of each type. DTAlite coupled with MOVES Lite typically is applied to simulate traffic flow over a period of a few hours, such as during morning rush hour. DTAlite can be configured to represent a wide variety of traffic control measurements (TCMs) and traffic management strategies. The latter can include techniques for providing drivers with advisory information regarding traffic incidents and ways to divert to alternative paths through the network.

The new framework is one of the first integrated simulations of traffic activity and emissions that takes into account second-by-second vehicle activity, thereby enabling highly resolved estimation of link-based emissions taking into account variability in vehicle speed trajectories on different road segments throughout the network. DTAlite+MOVES Lite is therefore a highly cost effective computational tool for assessment baseline emissions on a link-by-link base for an urban area, and for exploring a wide variety of “what-if?” questions about how vehicle activity and emissions are influenced by traffic control measures and traffic management strategies. Thus, this EPA STAR grant has enabled development of a new technically rigorous modeling tool that can be used to address a wide variety of policy-relevant planning and evaluation questions. Example results demonstrate that improved traffic management can prevent a significant portion of emissions increases associated, for example, with traffic congestion related to traffic incidents that result in lane closure. The environmental benefits of the use of this new tool are yet to be fully realized, but there is potential for the new modeling framework to inform planning and operational management of transportation networks to prevent pollution and reduce energy consumption, while still providing individual drivers and passengers with the transportation services that they desire.


Journal Articles on this Report : 6 Displayed | Download in RIS Format

Other project views: All 30 publications 6 publications in selected types All 6 journal articles
Type Citation Project Document Sources
Journal Article Anya AR, Rouphail NM, Frey HC, Schroeder B. Application of AIMSUN microsimulation model to estimate emissions on signalized arterial corridors. Transportation Research Record 2014;2428(2):75-86. R834550 (Final)
  • Full-text: TRB-Full Text PDF
    Exit
  • Abstract: TRB-Abstract
    Exit
  • Journal Article Liu B, Frey HC. Variability in light-duty gasoline vehicle emission factors from trip-based real-world measurements. Environmental Science & Technology 2015;49(20):12525-12534. R834550 (Final)
  • Abstract from PubMed
  • Full-text: ES&T-Full Text HTML
    Exit
  • Abstract: ES&T-Abstract
    Exit
  • Other: ES&T-Full Text PDF
    Exit
  • Journal Article Liu B, Frey HC. Measurement and evaluation of real-world speed and acceleration activity envelopes for light-duty vehicles. Transportation Research Record 2015;2503:128-136. R834550 (Final)
  • Full-text: ResearchGate-Full Text PDF (prepublication)
    Exit
  • Abstract: TRB-Abstract
    Exit
  • Journal Article Salamati K, Coelho MC, Fernandes PJ, Rouphail NM, Frey HC, Bandeira J. Emissions estimation at multilane roundabouts: effects of movement and approach lane. Transportation Research Record 2014;2389:12-21. R834550 (Final)
  • Full-text: ResearchGate-Abstract & Full Text PDF (prepublication)
    Exit
  • Abstract: TRB-Abstract
    Exit
  • Journal Article Taylor J, Zhou X, Rouphail NM, Porter RJ. Method for investigating intradriver heterogeneity using vehicle trajectory data:a dynamic time warping approach. Transportation Research Part B-Methodological 2015;73:59-80. R834550 (Final)
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDirect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Zhou X, Tanvir S, Lei H, Taylor J, Liu B, Rouphail NM, Frey HC. Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies. Transportation Research Part D-Transport and Environment 2015;37:123-136. R834550 (Final)
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDirect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Supplemental Keywords:

    Emissions, energy, vehicles, multi-pollutant, uncertainty, greenhouse gases, criteria pollutants, mobile source air toxics, traffic simulation models, in-use measurement., Air, Air Quality, air toxics

    Progress and Final Reports:

    Original Abstract
  • 2010 Progress Report
  • 2011 Progress Report
  • 2012 Progress Report