Measuring Travel Time Reliability and Carbon Monoxide ......index (a measure of average congestion)...

NSF Research Experiences for Undergraduates (REU) in “Engineering Tomorrow” for Summer 2010

Preview for Project #1:

Measuring Travel Time Reliability and Carbon Monoxide Impact using Advanced Technologies

Prepared by Dr. Heng Wei, P.E. Department of Civil & Environmental Engineering

April 14, 2010

2010 Summer REU Program

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Preview Materials for 2010 Summer REU Project#1:

Measuring Travel Time Reliability and Carbon Monoxide Impact using Advanced Technologies

Prepared by Heng Wei ([email protected]), Qingyi Ai, Ph.D. Student; Vijay Krishna Nemalapuri, M.S. Student; and Zhuo Yao, Ph.D. Student

Goal and Objectives: The goal of the project is to explore the modeling method for integrating traffic flow operation with vehicle emissions by using the data obtained by advanced technologies. To fulfill the goal, the following objectives are designed to achieve through the project: (1) explore the method to measure travel time reliability using the travel trajectory data obtained by using the Global Positioning System (GPS) Travel Recorder Data Loggers; (2) explore the method to estimate Vehicle Specific Power (VSP) using the GPS-based travel trajectory data; and (3) explore the method for correlating VSP and vehicle emission (Carbon Monoxide – CO will be selected for the emission measurement).

Brief Description of the Methods: A designated route, as shown by Figure 1, will be selected as the study site for sample data survey.

Figure 1. Overview of the entire route for GPS survey

The following activities will be conducted: (1) travel trajectory data will be collected using the GPS Travel Recorder Data Loggers along the designated route for three designated

GPS Travel Recorder Data Loggers


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periods of time; (2) traffic will be videotaped and CO will be measured at a roadside site of the route, as shown by Figure 2; (3) obtained data will be extracted and summarized in office, including calculation of VSP on the route and at the roadside site, and integrated CO measurements with vehicle-classified traffic counts at the roadside site that are extracted from video by software VEVID; and (4) modeling analysis will be conducted, including travel time reliability by comparing route GPS data with the data which have been obtained in winter and spring by students of class CEE351 Transportation Engineering, VSP-based emission estimates based on the flowchart as shown by Figure 3, and calibration of the model representing the relationship between VSP and CO, which is recommended by Environmental Protection Agency (EPA).

Figure 1. CO and traffic sampling locations (1030 Cutter St, Cincinnati, OH 45203)

Hays-Porter Elementary School (all activities

must beyond the school property)

Retrieve vehicle classifications at the cross section

by video data

Placement of CO monitors near fence at out edge of I-75 South

Video camcorder installed at the parking lot with

power from the car battery


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Figure 3. Framework for VSP-based modeling of traffic operational impact on emission Expected Outcomes: (1) travel time reliability analysis results of three seasons (winter, spring and summer in 2010); (2) VSP analysis results along the designated routes; (3) VSP analysis results of the designated roadside site; (4) emission estimate at the designated roadside site; and (5) calibration of VSP vs. CO model (recommended by EPA). Final report is expected to include all above results. The results will be helpful for students to better understand the impact of traffic operation on the mobile-source CO emission under various traffic conditions. The students will also be guided to better understand mechanics of traffic flow and congestion, as well as factors influencing development and operation of sustainable transportation systems.

The research will be conducted in 8 weeks during the summer quarter. the students will also (1) report the progress once a week; and (2) the project team will submit a written biweekly progress report; and (3) PowerPoint presentation and a display poster will be presented on the last day which will be judged. The daily time schedule of the research activities in the calendar format for project #1 will be detailed at a later time prior to the beginning of the project on June 21st.

Video-extracted vehicle trajectory data

Vehicle count & classification

VSP Models

Acceleration/ Deceleration

Speed

Route GPS data

MOBILE/MOVES Models

Sta

tistic

al fl

eet

Dis

trib

utio

ns

OKI data source

Veh

icle

trav

eled

mile

s (V

MT

)

Emission factor vs. vehicle

type

New emission factor vs. VSP bin & vehicle

type

Computer data processing over defined periods of time

Emission of traffic fleets over dual-loop monitoring station

Grade


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Other preview readings are attached below, including

Preview Reading: Basics of Travel Time Reliability

Basics of Integrating traffic operation with emission impact

Designing On-Road Vehicle Test Programs for Effective Vehicle Emission Model Development

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Supplementary Reading for 2010 Summer REU Project

Basics of Travel Time Reliability

Traffic Congestion Is A Daily Reality In Most Of The Large Urban Areas In The United States http://ops.fhwa.dot.gov/publications/tt_reliability/brochure/

It's to be expected—large numbers of people all trying to reach their destinations at the same time,

usually during peak hours. Drivers are used to the everyday congestion and they plan for it. They don't like it, but they leave home early enough to get to work on time. It's the unexpected congestion that troubles travelers the most from day to day. A trip that usually takes a half-hour, with little or no warning, takes an hour.

Now the motorist is late for work, has missed a doctor's appointment, or is facing hefty childcare penalties for picking up the kids late. Maybe a trucker gets held up in unexpected traffic, making shipments late to the manufacturer, disrupting just-in-time delivery, and losing the competitive edge on other shippers.

Travelers want travel time reliability—a consistency or dependability in travel times, as measured from day to day or across different times of day. Drivers want to know that a trip will take a half-hour today, a half-hour tomorrow, and so on. Why Is Travel Time Reliability Important?

Most travelers are less tolerant of unexpected delays because such delays have larger consequences than drivers face with everyday congestion. Travelers also tend to remember the few bad days they spent in traffic, rather than an average time for travel throughout the year (see Figure 1).

Figure 1. Averages don't tell the full story

This figure shows two line charts, each depicting travel time over a year. The first chart's caption

says "How traffic conditions have been communicated" and shows a single flat line extending across the entire year, representing an annual average travel time. The second chart's caption says "What travelers experience" and shows a fluctuating line that represents the travel time for each day of the year. Three days with the longest travel times are highlighted, with a caption that reads "…and what they remember". In order to improve travel time reliability, the first step is to measure it. Measures of travel time reliability better represent a commuter's experience than a simple average travel time. For example, a typical before-and-after study attempts to show the benefits of an incident management program (see Figure 2). Looking at average travel time, the improvement may seem modest. However, travel time reliability provides a different perspective of the improvement: the worst few days have been dramatically improved. Travelers make it to their destinations on time more often or with fewer significant delays.

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Figure 2. Reliability measures capture the benefits of traffic management

This figure shows two line charts, each depicting daily travel times over a two-year period, with the first year representing travel times before a traffic management improvement and the second year representing travel times after a traffic management improvement. In the first chart, the improvement in average travel times is shown, and this improvement in average travel time is quite small. In the second chart, the improvement in travel time reliability is shown (based on the worst day of the month), and this reliability improvement is much better than the improvement in average travel time. How Do Agencies Measure Travel Time Reliability?

Travel time reliability measures are relatively new, but a few have proven effective. Most measures compare high-delay days to those with an average delay. The most effective methods of measuring travel time reliability are 90th or 95th percentile travel times, buffer index, and planning time index, explained in the following sections.

Several statistical measures, such as standard deviation and coefficient of variation, have been used to quantify travel time reliability. However, they are not easy for a nontechnical audience to understand and would be less-effective communication tools. They also treat early and late arrivals with equal weight. But the public cares much more about late arrivals. 90th or 95th percentile travel times

This method, the 90th or 95th percentile travel times, is perhaps the simplest method to measures travel time reliability. It estimates how bad delay will be on specific routes during the heaviest traffic days. The one or two bad days each month mark the 95th or 90th percentile, respectively. Users familiar with the route (such as commuters) can see how bad traffic is during those few bad days and plan their trips accordingly. This measure is reported in minutes. Buffer index

The buffer index represents the extra time (or time cushion) that travelers must add to their average travel time when planning trips to ensure on-time arrival. For example, a buffer index of 40 percent means that for a trip that usually takes 20 minutes a traveler should budget an additional 8 minutes to ensure on-time arrival most of the time.

Average travel time = 20 minutes Buffer index = 40 percent Buffer time = 20 minutes × 0.40 = 8 minutes The 8 extra minutes is called the buffer time. Therefore, the traveler should allow 28 minutes for

the trip in order to ensure on-time arrival 95 percent of the time.

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Planning time index

The planning time index represents how much total time a traveler should allow to ensure on-time arrival. While the buffer index shows the additional travel time that is necessary, the planning time index shows the total travel time that is necessary (see Figure 3).

Figure 3 shows a line chart of the travel time index and planning time index by time of an average day for citywide conditions in Los Angeles. The line representing the travel time index has two peaks that correspond with the morning and evening peak traffic times. The line representing the planning time index tracks a similar trend, but has much high index values. This is because the travel time index approximates the 50th percentile of travel conditions, whereas the planning time index represents the 95th percentile. The space between the planning time index and the travel time index is identified as the "buffer between the expected (avg.) and 95th percentile travel times."

Figure 3. Reliability measures compared to average congestion measures (Source: http://mobility.tamu.edu/mmp/)

For example, a planning time index of 1.60 means that for a trip that takes 15 minutes in light

traffic a traveler should budget a total of 24 minutes to ensure on-time arrival 95 percent of the time. Free-flow travel time = 15 minutes

Planning time index = 1.60 Planning time = 15 minutes × 1.60 = 24 minutes The planning time index is especially useful because it can be directly compared to the travel time

index (a measure of average congestion) on similar numeric scales. The travel time index is a measure of average conditions that tells one how much longer, on average, travel times are during congestion compared to during light traffic.

Figure 3 illustrates the relationship between the buffer index and the planning time index. The buffer index represents the additional time that is necessary, whereas the planning time index represents the total travel time that is necessary.

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2010 Summer REU 2010 Summer REU ReadingReading

Basics of IntegratingBasics of Integrating Traffic Operation withTraffic Operation with

Heng Wei, Ph.D., P.E., Heng Wei, Ph.D., P.E., Assistant Assistant ProfessorProfessorDepartment of Civil & Environmental EngineeringDepartment of Civil & Environmental Engineering

University of Cincinnati , USAUniversity of Cincinnati , USA

Basics of Integrating Basics of Integrating Traffic Operation with Traffic Operation with Emission Impact Emission Impact

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2010 Summer REU 2010 Summer REU ReadingReading Transportation Problems: Transportation Problems:

Congestion and SafetyCongestion and SafetyCongestion has been getting worse in America’s 437 cities of all sizes since 1982, and only in year 2005 congestion caused dwellers in those urban cities to travel an additional 4.2 billion hours and to purchase pan extra 2.9 billion gallons of fuel for a cost of $78 billion.

- Schrank, D. and Lomax, T. The 2007 Urban Mobility Report. Sept. 2007

More deaths on roadways • About 1.2 million people worldwide are killed and 50 million are

injured on roadways through the world annually.• In United State, over 40,000 people are killed in motor vehicles

h h

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crashes each year.• In 2006, there were 334,206 crashes in Ohio – 1,239 people were

killed and 122,979 people were injured. In addition to the emotional impact, the economic cost to Ohio is about $10 billion per year in lost wages, increased health care and other related costs. (www.dot.state.oh.us)

• More motorists die on rural roads: http://www.usatoday.com/news/nation/2009-10-06-more-die-on-rural-roads_N.htm?loc=interstitialskip

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2010 Summer REU 2010 Summer REU ReadingReading Transportation Sustainability Concern?

Challenges in traffic congestion, safety, and environmental impact

• Car-based urban transport systems have proved unsustainable, consuming excessive energy, affecting the health of populations and delivering a declining level of service despite increasing investments Manydelivering a declining level of service despite increasing investments. Many of these adverse impacts fall disproportionately on those social groups who are also least likely to own and drive cars.

• Low benefit cost ratio of private transportation versus public transportation.• Sprawling cities and longer car trips versus traditional urban

neighborhoods and walk, cycle and use transit.• Adequate and new transport infrastructure needed to be rehabilitated or

built.

2010 Summer REU 2010 Summer REU ReadingReading Existing Disciplines of Existing Disciplines of

Transportation SystemsTransportation Systems

• Transportation Engineering: traffic control, operation and management.g

• Highway Engineering: highway infrastructure design, alternative cost analysis.

• Transportation Planning: demand forecasting -- trips attributed by land use, social economic factors and transportation network infrastructures.

Intelligent transportation systems (ITS): The application of advanced sensor, computer,

Gelectronics, and communication, Geographic Information Systems (GIS)/Global Positioning System (GPS) technologies and management strategies - in an integrated manner – to increase the safety and efficiency of the surface transportation system.

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2010 Summer REU 2010 Summer REU ReadingReading TrafficTraffic--Generated Air PollutantsGenerated Air Pollutants

• The tailpipe emissions of vehicles, as a consequence of The incomplete burning of carbon in fuels such as gasoline or diesel produces, include: carbon monoxide (CO) particulate matter (PM) hydrocarbons (HC) nitrogen oxides (NOx) carbon dioxide (CO2)

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( 2) volatile organic compounds (VOC)

• Particulate Matter (PM2.5): a mixture of solid particles and liquid droplets found in the air.


• Transportation greenhouse gases (GHGs) emissions are primarily in the form of CO2, increase of which may prevent heat radiation/loss from the earth to the space and cause climate

Factors influencing Global Factors influencing Global Climate ChangeClimate Change

radiation/loss from the earth to the space and cause climate change.

• The highway vehicle emission is the main source for CO2. Heavy duty vehicles contribute to 23% of the on-road GHGs emissions, out of which 97% is related to freight haulage.

• Need to reduce GHGs emission from transportation & other human activities :

- carbon dioxide (CO2)carbon dioxide (CO2)- methane (CH4)- nitrous oxide (N2O) - black carbon?

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2010 Summer REU 2010 Summer REU ReadingReading Factors influencing human healthFactors influencing human health

• Elevated levels of air pollutants (e.g., CO, PM, and NOx ) from traffic exhaust near major roadways are associated with adverse health effects in children, such as respiratorywith adverse health effects in children, such as respiratory allergies, decreased lung function and development, bronchitis, and asthma exacerbation.

• Truck routes generally run along major roadways aerosolizing harmful particles (e.g., diesel exhausts PM). Diesel fuel powered school buses also represent a major exposure source.

• Proximity to the pollution source is a reliable estimate of exposure.

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y p p


• Ozone (O3): Ground-level ozone is the major component of smog. Ground-level ozone is noxious pollutant, not directly emitted, but is formed by the reaction of NOx and volatile organic compounds (VOC)

Ozone (Ozone (臭氧臭氧))

formed by the reaction of NOx and volatile organic compounds (VOC) in the presence of sunlight.

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5


(Compressed

9

natural gas)

2010 Summer REU 2010 Summer REU ReadingReading Transportation conformity

• Goal under the Clean Air Act:Clean Air Act: control and reduce adverse environmental impact of traffic-generated air pollution.

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• Localized analysis of traffic‐generated pollutants (e.g., CO) is often required by the U.S. Environmental Protection Agency (EPA) to determine project‐level air conformity of transportation projects in accordance with State Implementation Plan (SIP).

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Fact Figure of EmissionFact Figure of Emission

Refueling EmissionsEvaporative Emissions

Hot Soak Running LossesDiurnal

CO2

Estimated based on fuel consumption

Combustion EmissionsNOx - High temperatures from

combustionCO - Incomplete combustionHC - Escaping fuels

Two ProcessesCombustion (Exhaust System)Evaporation (Fuel Storage and

Delivery System)

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2010 Summer REU 2010 Summer REU ReadingReading Air Quality StandardsAir Quality Standards

National Ambient Air Quality Standards (NAAQS) are set by EPA to protect public health and welfare. Primary standards are designed to protect against adverse health effects, while secondary standards protect against welfare ff t h d t t ti b ildi d d d i ibilit

An area is in violation of a standard if it exceeds the concentration level for its evaluation time frames. For example, for an area to attain the 8-hour ozone standard, the 3-

effects, such as damage to crops, vegetation, buildings, and decreased visibility.

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year average of the fourth-highest daily maximum 8-hour average ozone concentrations measured at each monitor within the year must not exceed 0.08 ppm.

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• Emission Equivalence= emission factor × vehicle travel activity (speed, VMT) × other correction factors

Traffic Emission Modeling Traffic Emission Modeling

VMT) other correction factors• Total vehicle fuel consumption

= vehicle travel activity × fuel intensity (e.g., litres/100km)

• Total vehicle emissions = Fuel consumption × emission equivalences

Emission factor approaches:

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Approach 1: Generated from a vehicle emission model (MOBILE, MOVES, EMFAC)

Approach 2: Derived from a speed-emission curve, table or equation

Approach 3: Estimated from vehicle fuel consumptions

2010 Summer REU 2010 Summer REU ReadingReading Emission Factors: SpeedEmission Factors: Speed--

Emission CurveEmission Curve

14FHWA vehicle speed-emission curve

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2010 Summer REU 2010 Summer REU ReadingReading Emission Factors: Vehicle Emission Factors: Vehicle

FFuel Consumptionuel Consumption

Travel model outputs Fuel consumption GHG emissionsVMT/VKT, vehicle type, average speed by fuel type CO2, CH4, N2O

Speed-fuel consumption curve

15Driving 55mph -- Is it the most efficient speed?


How MOVES Handles How MOVES Handles VVehicle Activity?ehicle Activity?

• Vehicle Specific Power (VSP) – the instantaneous power per unit mass of the vehicle, which is generated by the engine to overcome the rolling resistance and aerodynamic drag, and to increase the kinetic and potential energies of the vehicle; a measure of the energy the vehicle is using at a moment in time

– Speed, acceleration, and road grade are key factors influencing fuel consumption for diesel and hydrogen fuel cell buses under real-world operating conditions. VSP is a proxy variable for engine load that has been shown to be highly correlated with emissions.

– Affected by acceleration, road grade, resistance, etc.• Operating Mode – what the vehicle is doing, i.e., accelerating,

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braking, cruising, idling– Vehicles use different VSP in different operating modes– MOVES defines 23 operating mode bins – combinations of speed and

VSP for different running conditions plus additional operating modes for starts and evaporative emissions

• Drive Cycle – a second-by-second description of vehicle activity over time, typically including multiple operating modes

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Vehicle Specific Power (VSP)Vehicle Specific Power (VSP)

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Vehicle Specific Power (VSP)Vehicle Specific Power (VSP)

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2010 Summer REU 2010 Summer REU ReadingReading Emission vs. VSPEmission vs. VSP

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2010 Summer REU 2010 Summer REU ReadingReading Emission vs. VSPEmission vs. VSP

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2010 Summer REU 2010 Summer REU ReadingReading Air Pollution Dispersion Air Pollution Dispersion

ModelingModeling

• Air quality models are used to predict ground level concentrations down point of sources. The object of a model is to relate mathematically the effects of source emissions on

d l l t ti

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ground level concentrations, and to establish that permissible levels are, or are not, being exceeded.

2010 Summer REU 2010 Summer REU ReadingReading Air Dispersion ModelsAir Dispersion Models

• Gaussian

• Numerical

St ti ti l ( i i l)

• Statistical (fluctuation)

• Box

Ph i l• Statistical (empirical)

Modeling Techniques

• Physical

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Forms of Gaussian (“Normal”) Forms of Gaussian (“Normal”) Distribution Model Distribution Model

2

2/1

)(exp

)2(

1)(

xxf

680)( df

Single Distribution

1)( dxxf

68.0)( dxxf

2

2

95.0)( dxxf

),()()( zyfzfyf

z

z

y

y

zy

zyzyf

22 )()(exp

2

1),(

Double Distribution

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Gaussian Dispersion EquationGaussian Dispersion Equation

Ambient concentration is a function of emissions, downwind, lateral, and relative vertical distance from the source, cross-wise distance from the flow direction, wind speed, and PGT stability class.

i Al i d i C i d i i l

, p , y

x-axis - Alongwind; y-axis - Crosswind; Parameters:C = Concentration (g/m3)Q = Emission Rate (g/s)u = Stack-top wind speed (m/s)y = Crosswind dispersion coefficient (m)

z-axis - Vertical

z = Vertical dispersion coeff.(m)y = Crosswind distance (m)z = Above-ground height (m)H = Effective plume height (m)

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Gaussian Plume Gaussian Plume

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Horizontal dispersion coefficientcoefficient

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Vertical dispersion coefficient

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2010 Summer REU 2010 Summer REU ReadingReading Gaussian Finite Gaussian Finite

Line Source ModelLine Source Model

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Intersection ModelIntersection Model

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• Series of models developed to provide better estimations of motor vehicle pollutant

CALINE ModelCALINE Model

motor vehicle pollutant concentrations near highways and arteries.

• Main features:

– Finite line segment approach

– Mixing zone concept to incorporate traffic inducedincorporate traffic induced dispersion

– New dispersion data near highways, adjustments for averaging time and surface roughness included for P-G-T coefficients

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2010 Summer REU 2010 Summer REU ReadingReading CALRoadsCALRoads ViewView

• CALRoads View is an air dispersion modeling software package for predicting air quality impacts of pollutants near roadways. CALRoadsp g q y p p yView features three mobile source dispersion models: CALINE4, CAL3QHC, CAL3QHCR.

• CALINE4: Predicts air concentrations of carbon monoxide (CO), Nitrogen Dioxide (NO2), and suspended particles near roadways. Options are available for modeling near intersections, parking lots, elevated or depressed freeways and canyons.

• CAL3QHC: Estimates total air pollutant concentrations (CO or PM) near highways from both moving and idling vehicles This model also

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near highways from both moving and idling vehicles. This model also estimates the length of queues formed idling vehicles at signalized intersections.

• CAL3QHCR: An enhanced version of CAL3QHCR, this model can process up to a year of hourly meteorological data and vehicular emissions, traffic volume, and signalization (ETS) data for each hour of a week.

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Preliminary Case Study Preliminary Case Study

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2010 Summer REU 2010 Summer REU ReadingReading Connecting Traffic, air pollution and Connecting Traffic, air pollution and

exposure risk to air pollutantsexposure risk to air pollutants

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Estimating Emission Factors:• Vehicles moving below 2.5 mph are considered to be idling

that require idle emission factor


that require idle emission factor• All other vehicles contribute to running emissions

Traffic Variables

Meteorological Variables

MOBILE6.2

Idle emission factor

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Variables

Engine Specifications & Fuel Standards

Running emission factor

Dispersion Analysis:


Idle emission factor

Running emission factor

CAL3QHC

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Topography in terms of geographical coordinates

CALRoadsView interface

Traffic Operations

Meteorology

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Vehicle Specific Power for link based emissions:

INDEX DATE TIMELATITUDE N/S

LONGITUDE E/W

ALTITUDE SPEED

1 6/23/2009 12:08:37 39 1355 N 84 52171W 205 5716 34 70079

GPS data


1 6/23/2009 12:08:37 39.1355 N 84.52171W 205.5716 34.70079

2 6/23/2009 12:08:39 39.13547 N 84.5215W 202.5783 33.39065

3 6/23/2009 12:08:41 39.13539 N 84.52124W 196.8852 28.2295

4 6/23/2009 12:08:43 39.13536 N 84.52109W 195.0979 22.33874

5 6/23/2009 12:08:45 39.13536 N 84.52097W 194.3447 15.22546

6 6/23/2009 12:08:47 39.13542 N 84.52091W 194.8382 7.186235

7 6/23/2009 12:08:49 39.13542 N 84.5209W 194.2711 1.88474

8 6/23/2009 12:08:51 39.13541 N 84.52091W 193.1873 1.586848

9 6/23/2009 12:08:53 39.13542 N 84.52091W 191.9721 3.210386

10 6/23/2009 12:08:55 39.13543 N 84.52091W 190.927 2.561167

11 6/23/2009 12:08:57 39.13544 N 84.5209W 190.0956 3.868767

12 6/23/2009 12:08:59 39.13545 N 84.52083W 190.7136 13.78211

13 6/23/2009 12:09:01 39.13544 N 84.52068W 192.5479 25.72884

GPS DATA

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14 6/23/2009 12:09:03 39.1354 N 84.52049W 192.0667 34.40069

15 6/23/2009 12:09:05 39.13535 N 84.52024W 193.2574 38.38475

16 6/23/2009 12:09:07 39.13533 N 84.51998W 193.3353 42.71485

17 6/23/2009 12:09:09 39.13529 N 84.5197W 192.2708 46.02012

18 6/23/2009 12:09:11 39.13526 N 84.51939W 191.7158 50.35645

19 6/23/2009 12:09:13 39.13522 N 84.51906W 191.2935 53.21958

20 6/23/2009 12:09:15 39.13518 N 84.51869W 190.4087 58.84002

21 6/23/2009 12:09:17 39.13513 N 84.5183W 187.8115 62.31138

22 6/23/2009 12:09:19 39.13509 N 84.51788W 185.9928 65.19372

23 6/23/2009 12:09:21 39.13505 N 84.51747W 184.2512 65.35709

24 6/23/2009 12:09:23 39.13506 N 84.51704W 183.3863 63.93864

]

UTM conversion

Location information, grade, acceleration, instantaneous speed

Emission factors from activity based MOBILE6.2


VSP:

Link based dynamic emission f

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factors

• GPS data also helps us in understanding actual speed profile for the roadway

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Receptor(R0)

S iWind

Results: • CAL3QHC performed well in predicting CO concentrations, with under prediction at receptor


V hi l S ifi P (VSP) i i t t t i

ScenarioDirection

(degrees)

Speed

(m/s)

CO

modeled

CO

observed

S1 50 2 0.5 0.7338

S2 10 4 0.2 0.4177

S3 20 6 0.1 0.0929

S4 10 8 0.1 0.0658

p psites

• An Index of Agreement of 0.081 suggests a close match between the predicted and observed results but an RMSE of 1.115 suggests that more tests are needed

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• Vehicle Specific Power (VSP) is an important parameter in characterizing link based emissions.

• Minimal correlation has been found between CO concentrations and individual elements such as acceleration, grade and speed.

• Factors such as temperature phenomena on gases, and control delay functions have to be studied . In addition to this, health effects are to be integrated.


Question?Question?

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Designing On-Road Vehicle Test Programs for Effective Vehicle Emission Model Development

Theodore Younglove1, George Scora2, and Matthew Barth2

1The Statistical Consulting Collaboratory, University of California, Riverside, CA 92521 tel: (951) 827-7939, email: [email protected]

2Bourns College of Engineering-Center for Environmental Research and Technology, University of California, Riverside, CA 92521, tel: (951) 781-5791, fax: (951) 781-5744, email: [email protected]; [email protected]

TRB Paper No. 05-2770 Submission date: July 30th, 2004; revised: November 16th, 2004; finalized: March 15, 2005 Revised for Transportation Research Record: 4/1/2004 Word count, including abstract, references, figures and tables: 6645 Abstract word count: 203

ABSTRACT

Mobile source emission models for years have depended on laboratory-based dynamometer data. In recent years however, portable emission measurement systems (PEMS) have become commercially available and in widespread use, making on-road real-world measurements possible. As a result, the newest mobile source emission models (e.g., U.S. EPA’s Mobile Vehicle Emission Simulator) are becoming increasingly dependent on PEMS data. Although on-road measurements are made under more realistic conditions compared to laboratory-based dynamometer test cycles, they also introduce additional influencing variables that must be carefully measured in order to properly develop emission models. Further, test programs that simply measure in-use driving patterns of randomly selected vehicles will result in models that can effectively predict current-year emission inventories for typical driving conditions. However, when predicting more aggressive transportation operations than current typical operation, (e.g., higher speeds, accelerations, etc.), the model predictions will be less certain. In this paper, various issues associated with on-road emission measurements and modeling are presented. Further, we examine an example on-road emissions dataset and the reduction in estimation error through the addition of a short aggressive driving test to the in-use data. Based on these results, recommendations are made on how to improve the on-road test programs for developing more robust emission models.

Keywords: vehicle activity patterns, portable emission measurement systems

Younglove/Scora/Barth 1

1. INTRODUCTION

Over the last several decades, there has been a significant amount of activity in developing increasingly accurate mobile source emission models. Predicting emissions from motor vehicles is an integral part of many programs aimed at improving air quality in non-attainment regions of the U.S. The Clean Air Act Amendments (CAAA) of 1990 and subsequent transportation funding bills place great emphasis on modeling to provide accurate accounting of progress toward meeting air quality goals and deadlines, that if not met could lead to highway funds being withheld. Congestion mitigation and transportation management strategies will only be possible if it can be shown that their implementation will not further degrade the air quality in specific urban areas.

The primary mobile source emission models developed for regulatory purposes have been the U.S. Environmental Protection Agency’s (U.S. EPA) MOBILE model and California Air Resources Board’s (CARB) EMFAC model. Both of these models have incrementally improved over the years with updated versions. These models were initially developed based on specific certification driving cycles that were assumed to represent average or typical driving. Base emission rates were developed primarily from laboratory dynamometer tests running these certification cycles. In the mid-1990s, it was recognized that these certification cycles were not very representative of modern traffic driving patterns, leading to the development of improved driving cycles such as CARB’s Unified Cycle (1) and U.S. EPA’s SFTP (supplemental federal test procedure, see (2)).

Further, the traditional models of MOBILE and EMFAC were intended to predict emission inventories for large regional areas and are not very well suited for evaluating traffic operations that are more “microscopic” in nature, such as ramp metering, signal coordination, and many transportation control measures. Subsequently, the U.S. EPA recognized the need for a new emissions modeling framework that is capable of predicting emissions across various scales. As a result, the U.S. EPA is currently developing MOVES (Motor Vehicle Emission Simulator) which is intended to replace MOBILE and their NONROAD model (3, 4). MOVES will also have expanded capability, modeling additional emissions sources such as from aircraft, commercial marine, and locomotive, as well as addressing greenhouse gases. One of the major differences between the previous MOBILE series of models and MOVES is that MOVES will be based primarily on real-world, in-use vehicle emissions, as opposed to laboratory dynamometer-based data.

This shift from laboratory-based data to real-world in-use data coincides with recent improvements in on-board emission measurement technology. While available for several years, the instrumentation has grown in use and sophistication within the last few years. There are now several commercially available on-board emission measurement systems as well as several research programs that continue to improve the on-board measurement technology (e.g., see (5, 6, 7, 8, 9)). The advantage of on-board equipment is that you can now measure real-world emissions as the vehicles are driven on typical roadways under a variety of traffic conditions. These on-board emission measurement systems have also become quite portable, requiring just a few minutes to install in the target vehicles (10). Further, the accuracy and precision of some PEMS are quite high: capable of measuring LEV, ULEV, and SULEV certified vehicles.

Several on-road data collection programs have already begun and several more are planned in the near future. These on-road programs provide much more realistic data compared to strictly


laboratory testing. However, it is important to design the data collection program carefully so that the resulting data are maximally useful for emission model development. For example, it is crucial to recruit and instrument a statistically-sound sample of vehicles that a representative of the overall fleet. Further, it is important to collect emissions data from a wide-variety of driving conditions. In many cases drivers in real-world situations will not necessarily cover the entire performance “envelope” of the vehicles. If a model is to predict emissions under a variety of driving conditions (that is influenced by different levels of traffic conditions), it is important to collect data across a wide range of vehicle operation. A statistical examination of the influence of on-road factors on the variability and uncertainty of NOx from motor vehicles found substantial variability due to vehicle to vehicle differences as well as to driving cycles (11). In addition, an uncertainty analysis of MOBILE5 including temperature correction factors has been reported (12). More recently, NC State evaluated variability and uncertainty in emissions from mobile sources in their final report to the EPA as part of the MOVES data analysis shootout (13).

In this paper, we briefly outline key issues of on-road emissions data collection. This is followed by an analysis of a dataset of an initial on-road data collection program and show how data from limited vehicle operation can lead to high degrees of uncertainty in resulting emission models that need to predict emissions across a wide variety of conditions. Suggestions are made on how on-road data collection programs can be enhanced through some simple steps in order to ensure robust models.

2. ON-ROAD EMISSIONS DATA COLLECTION

Portable emissions measurement systems (PEMS) are very beneficial in obtaining data that are truly representative of real-world emissions. Laboratory dynamometer-based testing has been carried out for years and there have always been questions on how well the driving cycles used represent real-world conditions. Further, in the past a vehicle’s emission control systems have been designed to meet tailpipe emission certification levels, but they may not operate in the same manner during “off-cycle” events (e.g., hard accelerations, etc.). When performed correctly, on-board emissions testing will provide a wealth of information on real-world driving conditions.

PEMS units vary somewhat from manufacturer to manufacturer, but in general rugged models are available for use in on-road light duty applications as well as in off-road heavy duty applications with higher dust and vibration. They are designed to function under a wide range of ambient conditions, typically from near zero degrees Celsius up to 40 degrees Celsius. One constraint imposed by the current level of equipment is a relatively long warm-up times of up to an hour for the instrumentation. This results in the loss of some initial data or the use of careful protocols for data collection. Post processing of the data varies from instrument to instrument, but in general involves time alignment and conversion from concentration to mass measurement.

However, it is important to understand that on-road emissions data collection cannot completely replace laboratory-based programs. On-road data collection programs will result in real-world emissions data that are influenced by numerous factors including varied vehicle characteristics, traffic, driving behavior, road grade, and even meteorological conditions. All of these factors (and several more) affect tailpipe emissions and it is very difficult to control these factors individually. For example, if it was desired to carry out testing under specific temperatures, this is difficult to design in the real world. In the laboratory, it is somewhat easier to control specific factors individually so that the “confounding factors” issue is minimized.


Further, on-board emission measurement equipment currently measure specific tailpipe emission species (e.g., CO, HC, NOx, CO2, and particulate matter). Other pollutants (e.g., air toxics) are of equal or greater concern and currently can only be measured with laboratory-based equipment. It is also important to point out that the on-board equipment can only measure tailpipe emissions. With the improvement of emission control technology, evaporative emissions are becoming an increasing larger fraction of total vehicle emissions which still must be carefully managed. Evaporative emissions must be measured under closely controlled laboratory conditions.

As stated previously, it is difficult to control emissions-influencing factors during real-world testing. Even though they can be closely controlled, the different factors that influence vehicle emissions should at a minimum be measured along with the emissions data themselves. These data can then be used in the data analysis phase in identifying potential influencing factors. When designing an on-road emissions data collection program, there are several elements that need to be considered (14):

Vehicle Recruitment—it is crucial to recruit a wide variety of vehicles when carrying out data collection. In many cases, stratified random sampling techniques can be used based on having a priori knowledge of their potential emissions contribution. Various selection factors will include vehicle make, model, engine type and size, emission certification level, weight, and vehicle mileage. If possible, it is important to get a proper sampling of high emitting vehicles.

Study Area and Route Selection—for a given study area, it will be desired to include a variety of location-specific factors including different roadway facility types (e.g., highway, arterial, residential roads), specific roadway facility elements (e.g., High Occupancy Vehicle (HOV) lanes, different intersection types, toll booths, etc.), different levels of traffic congestion, and influence of road grade.

Temporal Issues—in many locations, the time of year, time of week, and time of day will play a role in vehicle emissions. For example, summertime emissions may be very different from colder wintertime emissions. Weekend travel will in many cases be different from weekday travel. Driving at peak traffic conditions compared to off-peak conditions will also have a significant influence.

Vehicle Operation—how a vehicle is operated will also play an important role in an on-road emissions study. If the resulting model will attempt to predict for a wide range of driving conditions, then the dataset should cover a wide range of conditions. If possible, a wide range of driving behavior (e.g., mild to aggressive) should be included. It may also be necessary to have a short amount of “calibration” testing be performed, as outlined in the following section.

For the remainder of this paper, we focus on the importance of obtaining a wide variety of vehicle activity when testing emissions. An initial on-road emissions dataset is examined in detail to show how lack of activity data at high power levels may lead to a large amount of model uncertainty.


3. U.S. EPA’S “SHOOTOUT” DATASET ANALYSIS

During the early development of MOVES, the U.S. EPA devised an “on-board emissions analysis shootout” program to evaluate potential methods for using on-board emissions data to generate emission rates (15). As part of this program, an on-board data collection effort was performed. On-board emission data (approximately 100,000 seconds) were gathered on 17 light-duty vehicles in the summer of 2001 in the Ann Arbor, Michigan area. Further, 15 heavy-duty diesel transit buses were also instrumented in the Fall 2001. All the light-duty vehicles in the sample were certified to federal Tier I tailpipe standards, with a model year range of 1996 through 2000, and had either 4 or 6 cylinder engines. The transit buses had a model year range of 1995 through 1997 and had similar mileage accumulation. Each vehicle was instrumented for a period of 1-3 days, during which period the owner was given no special instructions on how to operate the vehicle to ensure representative driving and operation patterns (15).

3.1. Vehicle Activity Measures

The light-duty vehicle operation data have been analyzed in detail. To illustrate the variety of vehicle activity, Figure 1 shows example acceleration-velocity contour histograms of the driving data. In these diagrams, it is possible to see both the breadth of speed and acceleration. For Figure 1a, the standard FTP driving cycle is represented. The FTP is well known for being a very mild driving cycle, first developed in 1974 (16). Figure 1b shows the histogram for vehicle 17 of the shootout dataset as an example of mild-activity conditions. This particular vehicle never went faster than 47 mph during the entire time it was instrumented in the study. Figure 1c and 1d show similar histograms for medium- and aggressive-activity patterns. Vehicle #13 has a significant amount of high-speed operation extending over 80 mph, while vehicle #14 has a mixture of low-, mid-, and high-speed driving. Figure 1e show the histogram for a fairly aggressive driving cycle, specifically the MEC01 which was developed for obtaining modal emission events as part of NCHRP Project 25-11 (17). In addition to focusing on specific modes of operation such as steady-state cruise, acceleration, deceleration, etc., the MEC01 cycle pushes the vehicle to its maximum performance limits with wide-open throttles and high-speed driving.

Another method of examining vehicle activity and its influence on emissions is to calculate Vehicle Specific Power (VSP). VSP is a convenient single measure (rather than a dual parameter histogram such as velocity and acceleration) that can be used directly to predict emissions. The VSP approach to emissions characterization was developed by several researchers (an example being Jimenez-Palacios (18)) and further developed as part of the MOVES model. VSP is a measure of the road load on a vehicle; it is defined as the power per unit mass to overcome road grade, rolling & aerodynamic resistance, and inertial acceleration:

VSP = v * (a*(1+γ) + g*grade + g*CR) + 2ρ*CD*A*v3/m where: v: is vehicle speed (assuming no headwind) in m/s a: is vehicle acceleration in m/s2

γ: is mass factor accounting for the rotational masses (~0.1) g: is acceleration due to gravity grade: is road grade CR: is rolling resistance (~0.0135)


ρ: is air density (1.2) CD: is aerodynamic drag coefficient A: is the frontal area m: is vehicle mass in metric tonnes.

Using typical values of coefficients, in SI units the equation becomes (CDA/m ~ 0.0005):

VSP (kW/metric Ton) = v * (1.1*a + 9.81*grade(%) + 0.132) + 0.001208*v3

If we examine the same example vehicle activity sets described above, we can plot histograms of VSP values as shown in Figure 2. Again, the FTP is seen to be fairly mild and the MEC01 cycle is very aggressive with a maximum value near 400 kW/metric ton. Vehicle #14 actually had higher VSP values than the MEC01, extending beyond 400.

3.2. VSP-Based Emission Measures

Several mobile source emission models (including MOVES) use VSP as the primary parameter for predicting emissions. From the initial work in MOVES, specific “VSP bins” were created for predicting emissions in each bin. These bin definitions are shown in Table 1. Using these bin definitions, we have taken the second-by-second driving measurements for the entire light-duty fleet of the shootout dataset and created VSP-bin histograms using CMEM modeled emissions, along with error bars that represent 95% confidence bands, shown in Figure 3. The actual vehicle emissions were replaced with CMEM modeled emissions for this analysis to remove vehicle variability from the examination of reductions in variability through additional testing. In addition, the use of modeled data allowed for comparison of additional test data that was not collected as part of the MOVES program without having to try and compensate for differences in test programs. The addition of a hard driving test cycle was then simulated for each vehicle by using CMEM to model the MEC driving cycle.

It can be seen in Figure 3 that the largest error bars are at the higher emission bins. This is due to the general a lack of emissions data for the higher power events. Based on these data alone, an emission model would have a higher degree of uncertainty for all driving cycles where a large amount of vehicle activity fell into these higher power bins. For example, if a transportation improvement project allowed for previously congested traffic to travel at higher speeds and accelerations, then the model would not be able to predict accurately since the model was previously “calibrated” primarily with lower-power emissions.

To avoid this potential pitfall, it is recommended that a small amount of prescribed high-powered driving take place as part of the on-road emission study. A small number of prescribed high-power events could be made part of the initial calibration step when the instrumentation is first placed in the vehicle.

The error bars in Figure 3 are based on a total of 71,892 seconds of driving time collected on the MOVES data analysis shootout vehicles using modeled emissions results. This represents a minimum variability situation because it removes the vehicle variability. The next step was to estimate the reduction in variability within VSP bins that could be achieved through the addition of a hard driving pre-or post data collection driving schedule. Say, for example that approximately 15 minutes of high power driving is added to each vehicles testing. This is the equivalent of


adding something like the first 900-second MEC01 cycle to the existing driving data. Without actually conducting the additional data collection, there are three main methods of illustrating this concept. The first would be to use additional data from other vehicles. The second would be to re-sample the data already collected on the vehicles in this paper, and the third is to use a second-by-second model to replicate the additional test data. Using additional data from other vehicles would inject vehicle-to vehicle variability into the problem, when we are trying to estimate the effect of additional data collection within vehicles. Re-sampling on the other hand was limited to replication of the limited number of seconds of data available in the high power levels and would under-represent the variability within the power bins because of the limits on data that have been previously discussed. CMEM modeling of the additional data was chosen as a compromise because it has lower variability than including data from other vehicles, but it does allow for filling out more points within the VSP bins than the actual vehicle data.

To show the effects of the additional data collection, we have taken the MEC01 cycle, created emission values using the CMEM model calibrated for the proper vehicle category (17), and added the emission values to the overall dataset. We then plotted the same VSP-binned emissions data to examine the effect on the error, as shown in Figure 4. Table 2 presents the percentage reduction in Standard Error by VSP bin for CO2, CO, HC, and NOx.

3.3. Cycle-Based Emission Measures

Variability of the emissions estimates is dependent upon the VSP bin. Data augmentation has a greater effect on lowering the variability of the estimates for higher power VSP bins, resulting in greater reductions in variability for harder driving cycles. Estimates of CO were generated for the FTP and MEC cycles using the bin data from the previous section, with upper and lower confidence limits calculated for both the on-road data and the augmented on-road data. Upper and lower estimates of the cycle total CO emissions were calculated by estimating the cycle totals using the upper limits of each VSP bin and the lower limits for each VSP bin. Data augmentation had a greater effect on the size of the confidence limits for the MEC cycle (Table 3).

4. RECOMMENDED TEST PROCEDURE

To enhance the utility of future on-road data sets, we propose the inclusion of an informal driving cycle which includes high power and high speed events. Having pre-specified second-by-second driving traces for on-road data is impractical, however a series of specific driving events could easily be added to the installation procedure prior to returning the vehicle to the owner for in-use data collection or conducted prior to removal of the PEMS unit from the vehicle. The additional driving would most likely be conducted by the equipment installation crew. The series of driving events should be conducted in a safe location and on a minimal grade.

The informal driving cycle consists of the following driving events:

• Accelerations – A series of accelerations up to a freeway speed of 65 mph, at 50% throttle, 75% throttle, 90% throttle, and wide open throttle.

• Extended decelerations – Three decelerations from a freeway speed of 65 mph to a complete stop.


• AC operation – Steady-state cruises having duration of 90 seconds and speeds of 25, 45, 55, and 70 mph with and without AC engaged.

Individual drivers do not necessarily cover the full range of vehicle operation needed for optimal model building in typical day-to-day use. This cycle, used in conjunction with a standard installation, would shorten the time necessary for instrumentation by removing the necessity of collecting sufficient data to capture the lower probability events at the very high and very low power levels. Addition of a simple series of driving events to the installation procedure can ensure data collection over the full range of vehicle performance. The addition of the augmented driving can greatly reduce the data collection time on individual vehicles for modeling purposes.

5. SUMMARY AND CONCLUSIONS

Collection of emissions data from typical in-use vehicles provides an unbiased data source for modeling of on-road emissions. However, many issues need to be considered when setting up a on-road emissions test program, outlined in Section 2. Among other issues, we have shown that simply instrumenting vehicles and collecting in-use driving data will typically have higher variability in the estimates of emissions for higher power VSP bins due to the reduced number of seconds of data usually available under high power conditions.

To avoid this potential pitfall, it is recommended that a small amount of prescribed high-powered driving take place as part of the on-road emission study. A small number of prescribed high-power events could be made part of the initial calibration step when the instrumentation is first placed in the vehicle. This is due to the fact that drivers in real-world situations do not always drive their vehicles under conditions that are represented by standard modal modeling driving cycles, such as hard accelerations and decelerations. By including a short driving procedure that could be performed immediately after installation or prior to removal of PEMS units in testing conditions, data could be collected that properly represent high-load events that are not currently captured by real-world driving of many motorists. The additional cost of collecting this data is low in comparison to the large benefits resulting from reductions in variability in the high-power bin emissions estimates.

The augmentation of the on-road data with the high-power augmentation data led to greater reductions in the size of the confidence intervals for the MEC cycle estimates than for the FTP cycle estimates. In general the use of a pre-specified driving schedule when collecting on-road data will produce greater benefits for reduction of model error on harder driving patterns than on milder driving.

An alternative to the in-use testing with the added pre-specified driving would be to just do the pre-specified driving on in-use vehicles without the extended in-use driving by the owner. This would produce data sets suitable for modal modeling, but would limit their use for inventory development. However, it would enable the rapid collection of data on a large numbers of in-use vehicles. REFERENCES

1. R. Gammariello, J.R. Long, (1996) “Development of Unified Correction Cycles” - CRC Sixth Annual On-Road Vehicle Emissions Workshop, San Diego, CA, 1996.


2. U.S. EPA, (1993) “Federal Test Procedure Review Project: Technical Report”, EPA Technical Report # 420-R-93-007, May 1993.

3. Megan Beardsley (2004) “MOVES Model Update”, Proceedings of the 14th CRC On-Road Vehicle Emissions Workshop, Hyatt Islandia, San Diego, California, March 29-31, 2004.

4. J. Koupal et al., (2002) “Draft Design and Implementation Plan for EPA's Multi-Scale Motor Vehicle and Equipment Emission System (MOVES)”, U.S. EPA Technical Report #420-P-02-006, October 2002.

5. Vojtisek-Lom, M., Cobb, J.T., (1997) “Vehicle Mass Emissions Measurement Using a Portable 5-Gas Exhaust Analyzer and Engine Computer Data”, Proceedings,Emission Inventory, Planning for the Future. Air and Waste Management Association, Pittsburgh, PA, 1997.

6. Scarbro, C. (2000) “An Investigation of ROVER’s Capabilities to Accurately Measure the In-Use Activity and Emissions of Late-Model Diesel and Gasoline Trucks”, Proceedings of the 10th CRC On-Road Emissions Workshop, San Diego, California.

7. R. Anderson (2004) “Development of a Novel On-Board Sampling and Conditioning System for the Measurement of Particulate Matter, Gases, and Air Toxics – Report on Phase 1”, Proceedings of the 14th CRC On-Road Vehicle Emissions Workshop, Hyatt Islandia, San Diego, California, March 29-31, 2004.

8. P. Witze (2004) “On-Board, Time-Resolved Diesel Particulate Measurements by Laser-Induced Incandescence”, Proceedings of the 14th CRC On-Road Vehicle Emissions Workshop, Hyatt Islandia, San Diego, California, March 29-31, 2004.

9. A. Shah and D. Booker (2004) “Advances in the Quartz Crystal Microbalance for In-Use Measurements on Diesel and Gasoline Powered Vehicles”, Proceedings of the 14th CRC On-Road Vehicle Emissions Workshop, Hyatt Islandia, San Diego, California, March 29-31, 2004.

10. M. Spears, (2004) “On-Vehicle Gaseous Emissions Measurements from Thirty-Two Light-Duty Diesel, Gasoline, CNG, and Hydrogen Fueled Vehicles”, Proceedings of the 14th CRC On-Road Vehicle Emissions Workshop, Hyatt Islandia, San Diego, CA, March 29-31, 2004.

11. Frey, H.C., “Variability and Uncertainty in Highway Vehicle Emission Factors,” Emission Inventory: Planning for the Future (held October 28-30 in Research Triangle Park, NC), Air and Waste Management Association, Pittsburgh, Pennsylvania, October 1997, pp. 208-219.

12. Frey, H.C., R. Bharvirkar, J. Zheng, “Quantitative Analysis of Variability and Uncertainty in Emissions Estimation”, Final Report, Prepared by North Carolina State University for Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, July 1999.

13. Computational Laboratory for Energy, Air, and Risk, Department of Civil Engineering, North Carolina State University (2002) “Methodology for Developing Modal Emission


Rates for EPA’s Multi-Scale Motor Vehicle and Equipment Emission System”, EPA Technical Report #EPA420-R-02-027, October 2002.

14. T. Younglove et al., (2002) “Mobile Source Emissions New Generation Model: Using a Hybrid Database Prediction Technique”, final report submitted to U.S. EPA, see http://www.epa.gov/otaq/models/ngm/cecert.pdf, Accessed July 2004.

15. C. Hart, J. Koupal, and R. Giannelli (2002) “EPA's Onboard Analysis Shootout: Overview and Results”, EPA Technical Report # 420-R-02-026, October 2002.

16. Federal Test Procedure. (1989) 40 Code of Federal Regulations, Parts 86-99, 1989.

17. Barth, M., F. An, T. Younglove, C. Levine, G. Scora, M. Ross, and T. Wenzel (1999) “The development of a comprehensive modal emissions model”, Final report submitted to the National Cooperative Highway Research Program, November, 1999, 255 p.

18. Jimenez-Palacios, J. (1999) “Understanding and Quantifying Motor Vehicle Emissions and Vehicle Specific Power with TILDAS Remote Sensing”, MIT Doctoral Thesis.


TABLE AND FIGURE CAPTIONS

Figure 1: Acceleration-velocity contour histograms of a) FTP driving cycle; b, c, d) example vehicle activity patterns for a mild-, medium-, and aggressive-activity vehicles respectively; and e) the MEC01 driving cycle.

Figure 2: Vehicle Specific Power histograms of a) FTP driving cycle; b, c, d) example vehicle activity patterns for a mild-, medium-, and aggressive-activity vehicles respectively; and e) the MEC01 driving cycle. Maximum values are shown as a single line.

Figure 3: Light-duty emissions as a function of VSP bins defined Table 1. Error bars indicate 95% confidence interval. a) CO2, b) CO, c) HC, d) NOx

Figure 4: Light-duty emissions (real-world + MEC01 data) as a function of VSP bins defined Table 1. Error bars indicate 95% confidence interval. a) CO2, b) CO, c) HC, d) NOx

Table 1: Vehicle Specific Power bins used in preliminary MOVES model (4).

Table 2: Percent reduction in standard error (S.E.) by VSP bin after inclusion of MEC data.

Table 3: Total cycle CO emissions estimate range (%) by cycle and data.


Figure 1: Acceleration-velocity contour histograms of a) FTP driving cycle; b, c, d) example vehicle activity patterns for a mild-, medium-, and aggressive-activity vehicles respectively; and e) the MEC01 driving cycle.


Figure 2: Vehicle Specific Power histograms of a) FTP driving cycle; b, c, d) example vehicle activity patterns for a mild-, medium-, and aggressive-activity vehicles respectively; and e) the MEC01 driving cycle. Maximum values are

shown as a single line.

Youngl

ove/Scora/Barth

Table 1: Vehicle Specific Power bins used in preliminary MOVES model (4).

VSP Bin Definition

(VSP in kW/Metric Ton)

1 VSP < -2 2 -2<= VSP <0 3 0<= VSP <1 4 1<= VSP <4 5 4<= VSP <7 6 7<= VSP <10 7 10<= VSP <13 8 13<= VSP <16 9 16<= VSP <19

10 19<= VSP <23 11 23<= VSP <28 12 28<= VSP <33 13 33<= VSP <39 14 39 <= VSP

13


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Figure 4: Light-duty emissions (real-world + MEC01 data) as a function of VSP bins defined Table 1. Error bars indicate 95% confidence interval. a) CO2, b) CO, c) HC, d) NOx

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VSP Bin CO2 Reduction in S.E. (%) CO Reduction in S.E. (%) HC Reduction in S.E. (%) NOx Reduction in S.E. (%)VSP <-2 50.00% 36.40% 36.67% 36.86%

VSP -2 to 0 25.00% 21.16% 21.05% 21.12%VSP 0 to 1 0.00% 13.92% 13.77% 14.06%VSP 1 to 4 22.22% 31.65% 31.78% 31.88%VSP 4 to 7 18.18% 19.52% 19.55% 19.54%

VSP 7 to 10 7.14% 0.00% 5.00% 5.02%VSP 10 to 13 28.57% 58.84% 27.79% 27.88%VSP 13 to 16 20.00% 23.14% 23.37% 24.12%VSP 16 to 19 20.00% 0.00% 26.48% 27.78%VSP 19 to 23 12.00% 33.33% 16.01% 17.38%VSP 23 to 28 34.38% 41.67% 35.52% 40.16%VSP 28 to 33 29.69% 29.41% 32.69% 61.15%VSP 33 to 39 39.39% 50.00% 61.28% 58.06%

VSP greater than 39 64.39% 68.09% 53.46% 84.58%

Table 2: Percent reduction in standard error (S.E.) by VSP bin after inclusion of MEC data.


CO Cycle Estimate Range(%)Cycle On Road Data Only On-Road Data AugmentedFTP 24.91% 18.40%MEC 12.06% 5.10%

Table 3: Total cycle CO emissions estimate range (%) by cycle and data.

Measuring Travel Time Reliability and Carbon Monoxide ......index (a measure of average congestion)...

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