For Presentation at the Air & Waste Management Association's 91st Annual Meeting & Exhibition, June 14-18, 1998, San Diego, California

Stopline Distributions of Speed and Acceleration for Signalized Intersections

98-TAA.02P (A272)

Shauna L. Hallmark, Randall Guensler, and John D. Leonard II

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0355

ABSTRACT

Current research suggests that vehicle emission rates are highly correlated with modal vehicle activity and that specific instances of load induced enrichment may contribute a disproportionate share of motor vehicle emissions. Consequently, a modal approach to transportation-related air quality modeling is becoming widely accepted as more accurate in making realistic estimates of mobile source contribution to localized and regional air quality.

By their nature, signalized intersections encompass a large proportion of modal activity experienced by motor vehicles in an urban area. Historically, air quality models for signalized intersections have utilized either average speeds or synthetic queuing and acceleration algorithms to simulate vehicle activity and subsequently emissions. Traffic simulation models are also capable of providing output profiles of speed and acceleration. However, they were created for purposes such as predicting traffic flow, not for accurately predicting modal profiles. To date, none of these models have been validated as tools for accurately predicting speed/acceleration profiles. Consequently, the accuracy of modal profile output from these models is unknown.

For more realistic input to air quality models, field studies using laser rangefinding devices were undertaken to quantify actual vehicle behavior along signalized links and at signal-controlled intersections. Data were analyzed to determine the fractions of vehicle activity spent in different operating modes, especially those that may lead to high engine load and elevated emissions. Analysis of field data in the vicinity of signalized intersections was undertaken and is presented. Intermediate results indicate that along roadway links near signalized intersections, hard accelerations (³ 6mph/s) that can lead to elevated emissions are frequent occurrences for passenger vehicles. Additionally, a majority of vehicle activity was observed that falls outside the range of activity included in the FTP. Final field study results compare measured vehicle activity to predicted activity from traffic engineering models. New studies and data will yield significantly improved simulation models for use in air quality analyses.

INTRODUCTION

Because of the magnitude of transportation-related emissions, mobile source emissions modeling is an integral part of air quality analysis by state and local agencies, especially as an input to emission inventories. These agencies depend on results from motor vehicle emissions models to demonstrate progress towards conformity and to gauge the air quality impact of new transportation projects. Consequently, the ability of models to accurately predict transportation-related emissions is critical, as the fate of future transportation projects and an area's ability to demonstrate conformity may hinge on these estimates.

A major shortcoming of current modeling is the aggregate representation of onroad vehicle activity to estimate emissions resulting in inaccurate characterization of actual driving behavior. Central to the current modeling philosophy is the assumption of average driving behavior. Average behavior assumes that all drivers engage in driving patterns similar to those over which vehicle emissions have been tested. Likewise, corresponding emission factors were developed from aggregate representations of vehicles based on the assumption that vehicles pollute similarly under an average range of speeds and vehicle miles traveled.(1) This traditional approach neglects variations in driving behavior, especially extremes such as hard accelerations or stop-and-go driving under congested conditions.

Numerous studies have show that under most on-road operating conditions, actual vehicle emissions can differ dramatically from what is predicted by current mobile source emission models.(2, 3; 4) Consequently, deviations from average driving behavior can produce dramatic increases in emissions. In some cases, the bulk of emissions from a vehicle trip may be attributed to a small fraction of the vehicle’s operation.

Current research indicates that vehicle emissions rates are highly correlated with actual vehicle activity. Further, research has indicated that a single "hard" acceleration event (enrichment event) may cause as much pollution as the remainder of the trip.(5) Emissions tests conducted at the California Air Resources Board, primarily on carbureted vehicles, showed a large increase of HC and CO during hard acceleration events. Later studies indicate that a single hard acceleration (> 6mph/s) could increase the total trip emission for carbon monoxide (CO) by a factor of two. Other tests on individual vehicles discovered that moderate to heavy engine loads lead to enrichment conditions that can increase gram/second emission rates for carbon monoxide by 2500 times and hydrocarbon emissions by 40 times compared to normal stoichiometric operation.(3; 6) Hard decelerations also appear to produce significant emissions.(5)

The current mobile source modeling regime is based on average vehicle behavior and average emission rates. However, as indicated previously, the way a vehicle is driven and the driving mode, such as acceleration versus steady cruising, can have a significant impact on the rate at which emissions are produced. For example, vehicle emission rates under free-flow conditions are significantly lower than emission rates when vehicles are undergoing significant accelerations, decelerations, and idling such as would are experienced by vehicle operating under response to traffic signals or congestion. Specifically, engine load is a variable that produces significant emission rate differences. Emissions associated with engine load are a function of vehicle type, engine type, vehicle velocity, acceleration (inertial load and wind resistance load), roadway grade, and accessory use such as air conditioning.(7)

To address shortcomings in current transportation-related air quality models and provide agencies with enhanced tools for vehicle emission estimates, various modal modeling approaches have been suggested. Modal models attempt to estimate emissions as a function of specific operating mode or engine load surrogates. To implement modal models, statistical distributions of vehicle activity corresponding to the amount of time that vehicles spend in different ranges of speeds and corresponding accelerations must be developed. Once vehicle activity is disaggregated into speed and acceleration distributions, activity-specific emission rates may be applied to estimate emissions. Modal emissions modeling is becoming widely accepted as a more theoretically accurate approach that will provide more realistic estimates of mobile source emissions contributions to local and regional air quality.(5, 6, 4; 8)

Although a modal approach to emissions modeling offers promising benefits in terms of accuracy, a weak link is the ability to realistically model onroad modal vehicle activity. Currently, little data exists relative to how vehicles operate in a real world setting. Current microscale dispersion models, such as CAL3QHC, CALINE4, or FLINT employ average speed inputs or predict average speeds based on synthetic queuing algorithms. Simulation models, such as TRANSYT7F or NETSIM, employ theoretical profiles of vehicle acceleration and speed relationships. Additionally, most models are incapable of integrating temporal and spatial characteristics of traffic and roadway characteristics.

To address the problems of lack of validated vehicle activity and to provide temporal and spatial resolution of vehicle activity to provide more realistic input to air quality models, field studies using laser rangefinding devices were undertaken to quantify actual vehicle behavior along signalized links and at signal-controlled intersections. Data were analyzed to determine the fractions of vehicle activity spent in different operating modes, especially those that may lead to high engine load and elevated emissions. Analysis of field data in the vicinity of signalized intersections was undertaken and is presented.

Air Quality Impacts of Signalized Intersections

No modal model can be complete unless it addresses the air quality impacts of signalized intersections. By their nature, signalized intersections encompass much of the modal activity experienced by motor vehicles in an urban area. Intersection vehicle activity includes deceleration into the intersection while stopping for a traffic control, time spent idling, and then acceleration out of the intersection until vehicles reach cruise speed. Some vehicles are not affected by the signal and maintain cruise speed throughout the intersection. Significant modal activity may also occur at other locations along signalized links experiencing heavy congestion that results in over-capacity stop and go conditions. Other locations of significant modal vehicle activity include freeway ramps and along freeway segments where vehicles undergo braking due to interference with other vehicles and rapid accelerations as vehicles merge with existing traffic. Although, freeway segments may be the source of most high speed, hard accelerations, signalized intersections and non-freeway roadway links make up the bulk of existing roadways and the majority of vehicle activity. Consequently, their impact on air quality using a modal approach should be considered.

The driving force behind development of a modal approach to transportation-related air quality analysis has been the need for accurate emission estimates for input to regional air quality models such as the Urban Airshed Model (UAM) for regional air quality. The ramifications of using current models versus those based on more accurate vehicle activity also extend to microscale air quality impact assessment. Microscale impacts are important for determination of violations of National Ambient Air Quality Standards (NAAQS) for carbon monoxide.(9) Al-Deek (10) suggests that as microscale air quality models are used to make critical decisions, it is imperative to validate estimates of those models. Barth, et al (4) and An (11), indicate that a model capable of predicting emissions based on vehicle operating mode is critical for evaluating "microscale" traffic scenarios such as effects of signal coordination as well as improving macroscale emission inventory predictions.

Localized air quality models such as CALINE4, CAL3QHC, or FLINT are used to predict emissions concentrations downwind from transportation facilities. Although most attempt to divide vehicle activity into ranges of specific activity, none are based on actual field-validated speed-acceleration profiles. CALINE4 requires operators to input average vehicle operating speed for each link and uses MOBILE5a average speed emission rates to estimate emissions. An intersection mode in CALINE4 allows operators to input link length, stopline distance, cruise speed, maximum and minimum idle time, acceleration time, deceleration time, and number of vehicles per cycle entering the intersection and delayed at the intersection per cycle. These parameters provide an indirect input of modal acceleration, deceleration, and idle activities. CALINE4 then employs modal corrections to emission rates based upon emissions testing performed in the 1970’s.(12) FLINT uses a deterministic queuing algorithm to determine queue lengths and idling time per vehicle. Roadway links are divided into zones of deceleration, idling, acceleration and cruise and coupled with idle and cruise emissions factors from MOBILE5a. Internal multipliers are used to approximate modal emissions.(13, 10). CAL3QHC considers locations where both cruise and idling occur and estimates emissions using idling and cruise emissions factors.(14) CAL3QHC does not address enrichment events such as acceleration or deceleration. Although, all three models use a highly aggregated form of modal modeling, they do not use actual speed or acceleration distributions to validate zones where different modal activity is expected to occur.

Purpose of Research

To validate actual vehicle behavior at signalized intersections and along roadway links between signals, a study is underway at Georgia Institute of Technology. Research is being conducted under a cooperative grant from the U.S. Environmental Protection Agency and the Federal Highway Administration.

The principal goal of the research is to accurately describe vehicle behavior on signalized links. Field data of vehicle activity profiles have been collected and statistical distributions of vehicle speed/acceleration activity as a function of operational and geometrical differences are being created. With collection and analysis of sufficient field data, statistical distributions of expected vehicle activity can be predicted. Statistical distributions of vehicle data are being developed that relate speed-acceleration profiles of vehicles to roadway characteristics such as grade, location along link, queue position, or volume of roadway to physical capacity.

An analysis of field-collected data characterizing modal activity at signalized intersections is presented below. These data serve as the base for a model that will be used in predicting vehicle emissions from signalized intersections. Modal activity along roadway links will be output in fractions of time spent in different speed-acceleration ranges. The activity can be linked with the modal emission rates to provide input to both regional and microscale air quality models..

Data Collection Methodology and Analysis

Results presented in this paper are based on vehicle activity data collected at nine different intersections in the Atlanta, Georgia area. Locations were selected to represent a range of different operational and geometrical conditions, including:

Individual vehicle activity profiles were collected in the field using hand-held laser rangefinding (LRF) devices. These laser guns are capable of measuring the distance to an object at a high sampling frequency (238.4 distance measurements per second) with a manufacturer’s accuracy specification of six inches (rms) over 2500 feet. Directionality of the laser beam allows the tracking of a single vehicle in a traffic stream facilitating the capture of a vehicle trace over a range of modal activity. Data for each vehicle observation are downloaded from the LRF and stored as a unique file on a PCMCIA card.(15) Data collected via the laser range finders were later processed utilizing a C program that calculated and output instantaneous speed, acceleration, time, and vehicle distance from the LRF. The program used a smoothing algorithm to filter out readings where interference occurred with the laser's lock on the vehicle being studied.(15)

Sampling was conducted at various areas along the signalized intersection roadways including:

In final data consolidation, instantaneous vehicle activity was grouped by distance from the intersection stopline. Intersection selection criteria included locations where both a constant grade existed throughout the intersection and approaches and geometric layout did not hinder accurate data collection. Turning movement counts were recorded simultaneously using JAMAR boards so that modal profiles could later be related by operational characteristics such as volume to capacity (v/c) if desired. Data collectors attempted to randomly sample vehicles by selecting the nth vehicle if possible to obtain a lock on the vehicle.

Attribute data for each vehicle were manually collected during the data collection process and later matched with output from the laser rangefinders so that observations of modal activity for individual vehicles could be sorted by lane, queue position, vehicle type, etc. In this manner, data could be binned by desired groupings. Data were sorted by location along a link so that critical locations for modal activity and possible enrichment can be identified. Locations near the intersection stopline, where the majority of modal activity is expected to occur, were specifically analyzed for presentation of results. Additionally, data were disaggregated by queue position and vehicle type as vehicles may operate differently based on queue position and type of vehicle. Vehicles were separated into two groups: passenger vehicles and heavy trucks. Cars, passenger trucks such as Blazers or Jeeps, and vans were all coded as passenger cars. Heavy trucks were identified as vehicles with six or more axles and one or more trailers.

Individual vehicle traces were reported in one-second intervals for the length of time the vehicle was tracked. With this information, a speed and corresponding acceleration could be identified for each second of activity. Data are divided into speed-acceleration bins (in 5.0 mph by 1.0 mph/sec bin increments). Instantaneous speed and accelerations can be sorted by location, vehicle group, queue position, etc. Table 1 shows sample output of processed LRF data from the C program.

Results

Analyses indicate that signalized intersections are characterized by significant modal activity and are locations where engine load may lead to significantly elevated emissions (enrichment). Actual instances of enrichment are determined by a number of factors including vehicle technology group, engine loading, power demand, grade, air conditioning use, etc. An absolute measure of enrichment events was not in the included in the scope of this paper. Instead, modal activity at signalized intersections is analyzed and locations and occurrences of extremes in vehicle activity where enrichment is likely to occur are presented. Acceleration events are divided into "hard accelerations" and instances of acceleration outside the range of the Federal Test Procedure (FTP). "Hard accelerations" are defined as those greater than or equal to 6 mph/s (9.7 km/h/s).(5) Instances of acceleration outside the range of the FTP are defined as those greater than or equal to 3.5 mph/s (5.6 km/h/s). Data were analyzed by queue positions since vehicles are expected to behave differently off of the stopline as a function of interference with surrounding vehicles.

Passenger Car Results

Shown in Table 2 are results of data analysis for passenger vehicles for various queue positions. "Thru" vehicles are vehicles that were not in a queue positions when they were tracked. In most cases, thru vehicles were not stopped by intersection signalization. For each queue position, data were extracted for a distance of 200 feet (30.5 m) from the intersection stopline. Total seconds of vehicle activity is the total number of vehicle seconds of activity recorded for the specified distance. Condensed field data output consisted of speed, acceleration, and location in one-second intervals for each vehicle tracked. Summing all instantaneous observations yielded total seconds of vehicle activity. Total seconds of vehicle activity may also include decelerations if they occurred in the specified area.

As illustrated in Table 2, 9% of total seconds of vehicle activity for the first vehicle in the queue were spent in accelerations ³ 6 mph/s (9.7 km/h/s). For the second vehicle, only 3% of total vehicle time was spent in hard accelerations. The 3rd vehicle in the queue and higher only spent 1% to 2% of vehicle activity in what has been defined as "hard acceleration". Of significant note is the percent of vehicle activity that was spent in operation outside the activity tested for the Federal Test Procedure (FTP). At the intersections studied, 31% to 42% of activity for the first and second vehicle at the stopline was outside the FTP range. For the third position and higher queue positions, over 17% to 22% of activity fell outside that range. Even for vehicles proceeding through the intersection without stopping, 7% of the time vehicle activity fell outside the speed/acceleration boundaries of the FTP. These findings support previous instrumented vehicle studies (undertaken as part of the FTP improvement process) that indicated more representative test cycles are necessary.(16)

Examples of three-dimensional plots of instantaneous passenger vehicle activity divided into bins of speed and acceleration by frequency are shown in Figure 1. Data were divided by queue positions and all queue positions are shown.

Figure 2 shows acceleration rate versus distance for the first vehicle in the queue, where distance is taken from the location where the vehicles stopped in the queue. Note how most of the higher velocities occur within 100 feet of the location from which the vehicle started. Figure 3 illustrates velocity versus the distance from the stopping point for these first departing vehicles. In Figure 4, velocity in miles per hour (mph) is shown versus acceleration in mph/s from the location where the vehicles stopped to the end of the distance where the laser gun was able to track the vehicles. Of interest are the speed ranges where most of the hard accelerations occur. Accelerations greater than or equal to 6 mph/s are found in the ranges from approximately 15 to 40 mph. Also of interest are the ranges of speeds. Speeds over 60 mph were recorded, indicating that signalized arterials in Atlanta are also locations of high speed as well as acceleration.

Heavy Truck Results

Heavy vehicle activity modes were also analyzed. Emissions modeling for heavy-duty diesel vehicles typically presume that emissions are a constant function of engine load (grams/bhp-hr). However, some newer computer controlled vehicles are hypothesized to deviate from the constant emission rate when operated under heavy loads. In addition, gasoline heavy-duty vehicles are expected to undergo enrichment in a similar manner to light-duty vehicles. Although heavy-duty engines are tested on an engine dynamometer cycle and not the FTP, for illustrative purposes the fractions of activity spent in ³ 6mph and outside the range of the light-duty FTP are presented in Table 3. Given the gearing of heavy-duty trucks, it is not surprising that a maximum of 1% of total activity recorded fell into the high acceleration range. The range of activity recorded that falls outside the FTP is from 0% to 9%. However, engine loads for heavy vehicles are significantly affected by acceleration and payload interaction, so the impacts of local road operations on heavy-duty vehicle emissions requires additional detailed analysis (ideally integrating weigh-in-motion data collection).

Figure 5 provides three-dimensional plots of instantaneous truck activity divided into bins of speed and acceleration by frequency for the first vehicle in the queue and through vehicles respectively. As was the case for passenger vehicles, data for all queue positions and thru vehicles are for 200 feet (24.4 to -6.1 m) from the stop bar. Although the activity predominantly falls within the confines of the FTP speed/acceleration profiles, there are significant differences noted between the modal activity associated with queue position. This indicates that when modal models are applied to heavy-duty vehicles on arterials, the queue position is likely to play an important role in estimated emissions.

CONCLUSIONS

The focus in transportation related air quality analysis is shifting from the current average speed modeling regime toward modal approaches. Vehicle activity, therefore, must be subdivided into specific activities where engine loading and elevated emissions are expected to occur. Results of recent research indicate that vehicle emission rates are highly correlated with actual vehicle activity and that specific instances of enrichment contribute a disproportionate share of motor vehicle emissions. To implement modal emissions models, actual vehicle activity must be quantified to facilitate identification of activities leading to engine loading resulting in enrichment.

Signalized intersections by their nature are characterized by substantial modal activity. In urban areas, roadways with signalization make up the bulk of total miles of roadway. Emissions along signalized roadways occur along both the roadway segment and in the area of the intersection. The analyses presented in this paper quantified modal vehicle activity only in the vicinity of signalization. However, the location along signalized links where the majority of modal activity is expected to occur is in the immediate vicinity of the intersections which was the focus of the analyses.

Field data of actual vehicle profiles of speed and acceleration were collected and analyzed to determine the amount and frequency of vehicle activity that may lead to enrichment conditions. Actual enrichment depends on numerous factors such as model year, vehicle type, air conditioner use, roadway grade, etc. However, locations of possible enrichment are identified. Data were analyzed by queue positions because vehicles are expected to behave differently depending on the amount of interference with surrounding vehicles. The research conducted in this study is the first step in developing a model that can be used to quantify actual vehicle modal activity and predict emissions at signalized intersections and along roadway links.

Nine different locations were studied in the Atlanta, Georgia metropolitan area. In the immediate area of a signalized intersection (within 200 feet (30.5 m)), hard accelerations appear to be frequent occurrences for passenger vehicles at these intersections. Results indicate that from 1% to over 9% of total vehicle activity, depending on queue position, are in the range of what may be considered a "hard" acceleration (³ 6mph/s (9.7 km/h/s)). A majority of vehicle activity was observed that falls outside the range of activity included in the FTP. This indicates that emission factors based on FTP baselines and corrections may actually reflect only a portion of real world driving conditions. Analysis of actual vehicle activity may also be useful in designing future driving cycles used in testing procedures, to more accurately replicate actual vehicle behavior.(17)

Velocity through intersections spanned a wide range, from 5 to 60 mph (8.1 to 96.6 km/h) for passenger vehicles and 0 to 45 mph (8.1 to 72.5 km/h) for heavy trucks. High speeds as well as hard accelerations characterize Atlanta intersections. With frequent episodes of high speed and "hard" acceleration activity, intersections are locations of significant modal activity. Because, this includes significant episodes of "hard" accelerations and instances of high speeds, the air quality impacts of signalized intersections may be greater than have previously predicted. This may have impacts on both localized and regional air quality. Given that frequent stops may lead to elevated emissions, a future approach to signal timing that minimizes stops and delays or reduces acceleration rates may be developed to mitigate air quality effects.

References

  1. Guensler, Randall, and Daniel Sperling; Congestion Pricing and Motor Vehicle Emissions: An Initial Review; In: Curbing Gridlock: Peak Period Fees to Relieve Traffic Congestion; Volume 2; National Academy Press: Washington, DC; 1994; pp. 356-379.
  2. Kelly, Nelson A. and Peter J. Groblicki; Real-World Emissions from a Modern Production Vehicle Driven in Los Angeles; Journal of the Air and Waste Management Association: Pittsburgh, PA; Volume 43; October 1993; pp. 1351-1357.
  3. LeBlanc, David C., Michael D. Meyer, F. Michael Saunders, and James A. Mulholland; Carbon Monoxide Emissions from Road Driving: Evidence of Emissions Due to Power Enrichment; Transportation Research Record; Number 1444; Transportation Research Board: Washington, DC; 1994; pp. 126-134.
  4. Barth, Matthew, Feng An, Joseph Norbeck, and Marc Ross; Modal Emissions Modeling: A Physical Approach; presented at the 75th Annual Meeting of the Transportation Research Board: Washington, D.C.; January 1996.
  5. Guensler, Randall; Data Needs for Evolving Motor Vehicle Emission Modeling Approaches; In: Transportation Planning and Air Quality II, Paul Benson, Ed.; American Society of Civil Engineers: New York, NY; 1993.
  6. Barth, Matthew, Theodore Younglove, Tom Wenzel, George Scora, Feng An, Marc Ross, and Joseph Norbeck; Analysis of Modal Emissions from a Diverse in-Use Vehicle Fleet; presented at the 75th Annual Meeting of the Transportation Research Board: Washington D.C., 1996; Paper Number 971179.
  7. Guensler, Randall, Michael O. Rodgers, Simon Washington, and William Bachman; An Overview of the MEASURE GIS-Based Modal Emissions Model; In: Transportation Planning and Air Quality III; Tom Wholley, Ed.; American Society of Civil Engineers: New York, NY; 1998.
  8. Washington, Simon; Considerations for Developing New Mobile Source Emissions Models; presented at the 75th Annual Meeting of the Transportation Research Board: Washington D.C.; January 1996; paper number 961359.
  9. U.S. Environmental Protection Agency; Office of Air Quality Planning and Standards; Guideline for Modeling Carbon Monoxide From Roadway Intersections; Research Triangle Park Report Number EPA-454/R-92-005; November 1992.
  10. Al-Deek, Haitham M., Roger L. Wayson, C. David Cooper, Deb Kelly, Richard Traynelils, P.S. Liu, L. Malone, and Amy Datz; A Queuing Algorithm for Calculating Idling Emissions in FLINT—the Florida Intersection Air Quality Model; presented at the 76th Annual Transportation Research Board Meeting: Washington, D.C., January 1997.
  11. An, Feng, Matthew Barth, Marc Ross, and Joseph Norbeck; The Development of a Comprehensive Modal Emissions Model: Operating Under Hot-Stabilized Condition; presented at the 75th Annual Meeting of the Transportation Research Board: Washington D.C., January 1996; paper number 970706.
  12. Benson, Paul; CALINE4 -Users Manual; 1989
  13. Wayson, Roger L., C. David Cooper, Haitham Al-Deek, Linda C. Malone, Amy Datz, Pwu-Sheng Liu, Deb Kelly, Richard Traynelils, Mahmoud Heriba, and Fouad Matar; FLINT--The 'Florida Intersection' Model for Air Quality Modeling; presented at the 76th Annual Transportation Research Board Meeting: Washington, D.C., January 1997.
  14. U.S. Environmental Protection Agency; User's Guide to CAL3QHC: A Modeling Methodology for Predicting Pollutant Concentrations Near Roadway Intersections; Version 2.0; Research Triangle Park; Report Number: EPA-454/R-92-006; 1992.
  15. Grant, Chris; Laser Rangefinder (Laser Gun) Standard Operating Procedure (SOP); Georgia Institute of Technology; May 1997.
  16. LeBlanc, David C., F. Michael Saunders, Michael D. Meyer, and Randall Guensler; Driving Pattern Variability and Potential Impacts on Vehicle CO Emissions; Transportation Research Record; Number 1472; Transportation Research Board: Washington, DC; 1995; pp. 45-52.
  17. Eisele, William L., Shawn M. Turner, and Robert J. Benz; Using Acceleration Characteristics in Air Quality and Energy Consumption Analyses; Technical Report 465100-1, Sponsored by The Office of the Governor of the State of Texas; August 1996.

TABLES

Table 1. Output from C program

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Laser Offset: 10.0’

Collection Date: 11-10-97

Start Time: 8: 3:0.00

Time= 2.55, Dist= 285.8, Speed= 40.5, Accel=-0.6

Time= 3.56, Dist= 344.3, Speed= 39.7, Accel=-0.8

Time= 4.56, Dist= 398.9, Speed= 36.9, Accel=-2.7

Time= 5.57, Dist= 448.6, Speed= 33.7, Accel=-3.3

# 1 Vehicle Type, Car

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Table 2. Modal Activity for Passenger Cars

Queue Position

Seconds of Vehicle Activity

% Time Spent in Accel ³ 6mph/s

% Time Spent Outside FTP

1

1138

9%

44%

2

466

3%

31%

3

252

1%

22%

4

152

2%

10%

5+

124

1%

17%

Thru

249

1%

7%

Table 3. Modal Activity for Heavy Trucks

Queue Position

Seconds of Vehicle Activity

% Time Spent in Accel ³ 6 mph/s

%Time Spent Outside FTP

1

326

0%

3%

2

172

1%

9%

3

100

0%

6%

4

68

0%

3%

5+

48

0%

0%

Thru

180

1%

4%

FIGURES

Figure 1. Three Dimensional Plots of Speed Versus Acceleration (10 feet before the Stopbar to 200 feet past the Stopbar)

 

Figure 2. Acceleration Versus Distance From Location Where Vehicle Stopped In Queue

Figure 3. Velocity Versus Distance From Location Where Vehicle Stopped in Queue

Figure 4. Velocity Versus Acceleration From Location Where Vehicle Stopped in Queue

Figure 5. Three Dimensional Plots of Speed Versus Acceleration for Heavy Vehicles(10 feet before the Stopbar to 200 feet past the Stopbar)

 


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