1 EVALUATION OF SIMULATION MODELS FOR PROJECT-LEVEL EMISSIONS MODELING Kyle P. Kosman1, Shauna L. Hallmark2, and Scott Poska3 1
Transportation Planner LSC Transportation Consultants, INC. 101 N. Tejon Street, Suite 200 Colorado Springs, CO 80903 (719) 633-2868 Email:
[email protected]
2
corresponding author Assistant Professor Department of Civil and Construction Engineering Iowa State University Ames, Iowa 50011 (515) 294-5249 Email:
[email protected] 3
Undergraduate Student Department of Civil and Construction Engineering Iowa State University Ames, Iowa 50011 (515) 294-5249 Word count: text: 5,599. 5 tables and 2 figures. Total: 7, 349
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2 ABSTRACT Project level transportation air quality analysis is used to evaluate the impacts of traffic flow improvements resulting from changes in signal timing or other roadway improvements. Simulation models are frequently used to evaluate changes in average speeds that result from traffic flow improvements since they are able to model relationships between vehicles rather than just giving estimate of travel time, delay, number of stops etc. This paper compares two simulation models, CORSIM and VISSIM. Their performance was evaluated for estimating the air quality impact of traffic signal improvements. CORSIM was compared to VISSIM for two different scenarios. First, output from the two models was compared to average and spot speed data collected in the field for two arterials. In the second scenario, their use in predicting emission reductions was evaluated for three CMAQ projects.
INTRODUCTION Project level transportation air quality analysis is used to evaluate the impacts of traffic flow improvements resulting from changes in signal timing or other roadway improvements. Impact assessment is used estimate net emission reductions to prioritize Congestion Mitigation and Air Quality (CMAQ) projects, evaluate the impact of traffic flow improvements as Transportation Control Management Strategies (TCMs), conduct project level conformity analysis, and to meet other regulatory requirements. Traffic signal improvements impact vehicle idling, acceleration, deceleration, and cruise speed. However, the impact of different timing strategies or roadway improvements is typically evaluated by either estimating reduction in the time vehicles spend idling or by estimating changes in average speed or using a combination of the two. This is based on available output from emission rate models. The most common emission rate model is the US Environmental Protection Agency’s (EPA) MOBILE series of models. MOBILE6.2 is the most current version available. Like its predecessors, MOBILE6.2 provides emission factors based on average vehicle speeds, which can be modeled from 2.5 to 65 mph (1). Although MOBILE does not specifically model emissions at zero speeds, idling emission rates can be approximated by using an average speed of 2.5 mph. Although emissions reductions from traffic flow improvements are currently based on improvements to average speed or reduction in vehicle idling, evidence suggests that emissions are correlated to specific vehicle operating mode and that emissions are higher for specific load events, particularly acceleration (2, 3, 4). Frey et al (5) evaluated the emission impacts of changes in traffic signal control using on-board emission sensing instruments. They reported that emissions (HC, NO, and CO) were significantly higher for acceleration than for idling or deceleration and that emissions were substantially influenced by traffic signalization primarily due to accelerations from vehicles stopping for a red light. As a result, acceleration may be the significant event to consider in traffic flow improvements. However, modal models which are capable of estimating changes in emissions due reduction in the number of acceleration events are not widely available. Nor does the EPA currently approve them for use. Consequently the majority of state
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3 and local agencies use either changes in average speed or reduction in time spent idling or a combination of the two to estimate emission impacts of traffic flow improvements. A number of computerized traffic analysis tools are available to estimate the impacts of different traffic signal timing strategies on intersection performance. Unlike dispersion or emission rate models, no specific traffic analysis model is currently recommended or required by the EPA for conformity or CMAQ analysis. Although no specific model is required, CMAQ guidelines indicate that a quantitative method to estimate emission reductions is necessary and must be based on a logical and reproducible analytical procedure (6). Traffic engineering analysis tools such as TRANSYT-7F, SYCHRO, or PASSER IV can be used to estimate queue length, average travel speed, and number of stops. However, such analysis tools use flow rate variables and deterministic relationships based on highway capacity and traffic flow research to model traffic movement on a section-by-section basis (7). Microscopic simulation models, including CORSIM, VISSIM, and WATSim , and Paramics model vehicles individually. Simulation models have been suggested as the best method to evaluate emission reductions from strategies that affect traffic flow since they are able to model interactions between drivers and variance of individual vehicle behavior in order to estimate changes in speed, delay, etc. travel time and resulting emissions. Consequently microscopic simulation models are able to model relationships between vehicles rather than just giving estimate of travel time, delay, number of stops etc. as provided by traffic engineering analysis tools (8, 9). Simulation Models Simulation models allow multiple scenarios to be tested. Variables are treated independently and system performance is predicted based on representations of temporal and spatial interactions between vehicles. Microsimulation results are also repeatable. Microscopic simulation models describe individual vehicle movement based on carfollowing and lane changing theory. Program logic controls how vehicles behave in terms of lane changes, passing maneuvers, acceleration, deceleration, gap acceptance, and execution of turning movements. Vehicles typically are able to respond to surrounding vehicles, pedestrians, and transit activity. Vehicles are represented individually and tracked second-by-second within the model and operational performance is uniquely calculated second-by-second (7). Simulation models are also readily available and generally can be used with both detailed data input and simple data input (which requires more dependence on model defaults). Simulation models were developed to model the impacts of signal timing, incidents, or design features on traffic flow. Under a number of situations, simulation models perform reasonably well. Chundury and Wolshon (10) found that the NETSIM module of CORSIM’s car-following theory performs reasonably well in routine driving conditions when compared to data collected in the field using GPS equipped test vehicles. Prevedouros and Wang (11) studied the performance of CORSIM, WATSim, and INTEGRATION for a larger freeway and arterial network. They reported that CORSIM and WATSim modeled average speeds and speed profiles fairly well, while INTEGRATION did not perform as well at modeling either average speeds or speed
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4 profiles. Mystkowski and Khan (12) found that CORSIM predicted similar queue lengths under high and medium volume to capacity (v/c) conditions as reported by field studies. CORSIM performed better overall for those conditions than traffic analysis models including SYNCHRO3, PASSER II-90, TRANSYT-7F, SIGNAL 94, although TRANSYT-7F performed similarly for high v/c conditions. However, not every aspect of micro-simulation programs have been well validated. For instance, simulation models employ theoretical profiles of vehicle acceleration and speed relationships, which are difficult to adequately validate since it is difficult to collect field data at the microscopic level (10). Additionally, most traffic simulation software assumes the same car-following behavior whether the vehicles are experiencing congestion or free-flow speeds (13). In a study conducted by (14) in a comparison of a multiregime traffic simulator (MRS), to CORSIM, using field data and the Highway Capacity Manual, it was found that CORSIM (which uses traditional carfollowing logic), produces much lower average speeds for vehicles on the network. Additionally CORSIM did not position vehicles in the proper lanes for mandatory lane changes. Although some studies found reasonable agreement between CORSIM average speeds and field studies, another study indicated that CORSIM modeled average speeds lower than field data (14, 15). Similarly, a comparison between NETSIM simulation and field data for intersections has shown that NETSIM does not adequately model vehicle acceleration (16). In another study, freeway conditions were tested using CORSIM, FREQ, and INTEGRATION. It was noted that model performance for all three became sporadic and unreliable under congested freeway conditions, although CORSIM was the most robust (7). Need for Research and Overview of Research Microsimulation models may be more appropriate for modeling the impacts of signal timing or operational improvements at signalized intersections than traffic analysis models since they allow stochastic modeling of individual vehicles to evaluate changes in conditions rather than only estimating speeds and delay. However, some deficiencies do exist that may impact average speed and idling estimates. Additionally, a number of microsimulation models are available with differing input requirements and assumptions that may result in different output that may affect project level emission analysis. The objective of this research was to evaluate and compare the performance of two microscopic simulation models for estimating the air quality impact of traffic signal improvements. Federal Highway’s CORSIM was compared to VISSIM for two different scenarios. First, output from the two models was compared to speed data collected in the field for two arterials. Second, their use in predicting emission reductions was evaluated for three actual CMAQ projects. CORSIM CORSIM was developed in the mid 1970’s as a microscopic simulation model capable of modeling surface streets, freeways, and basic transit operations. CORSIM is the most widely used microsimulation model in traffic engineering. CORSIM allows use of a range of traffic control devices including fixed time and actuated traffic signals with
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5 related surveillance systems, stop or yield-control, and ramp transitions. Vehicle behavior is guided by lane changing logic, car-following rules, and driver decisionmaking processes. CORSIM models and updates each individual vehicle’s parameters according to driver behavior and relationships to other vehicles, traffic signals, and events each second. Stochastic model parameters include vehicle characteristics, driver behavior, traffic characteristics, free-flow speed, and other attributes (12). Output includes delay queue length and time, number of stops, stopped delay, speeds, and other congestion based measures. Measures of effectiveness (MOEs) are calculated and can be reported on a lane-by-lane basis. VISSIM VISSIM is a stochastic microscopic simulation model capable of simulating traffic operations in urban areas with considerations on multi-modal travel such as public transportation in the form of bus and light-rail. It was developed at the University of Karlsruhe, Karlsruhe, Germany in the early 1970s. It allows 3-D viewing of simulation model results. Like CORSIM, VISSIM can model a variety of traffic signal control and provides similar MOEs (17).
CORSIM Versus VISSIM Both CORSIM and VISSIM are stochastic microsimulation models and both utilize an interval-based simulation approach. The two models have similar capabilities and logic. Most signal control systems can be modeled by both CORSIM and VISSIM. Bloomberg and Dale (18) compared CORSIM and VISSIM for six scenarios with congested arterial street conditions. They reported that relative travel times were similar for the two models. They also discussed differences in the two models. The main differences between the two include the following as discussed by Bloomberg and Dale (18): 1. Network Structure: CORSIM’s networks are based on a link-node representation where each link represents a one-directional segment connecting an upstream and downstream node. VISSIM’s networks are based on links and connectors between links. Each link represents a major street in the network and connectors, connect links together where appropriate. 2. Car-Following: CORSIM uses a set headway for individual drivers and vehicles attempt to maintain a minimum car-following distance without exceeding their assigned maximum speed. Parameters are updated each second in response to traffic control and other vehicles. VISSIM uses a psycho-physical model for driver behavior in which the vehicle engages in an iterative process of acceleration and deceleration as the driver of a faster-moving vehicle decelerates to match their perception of the slower vehicle’s speed and then accelerate when their speed falls below that of the slower vehicle. 3. Gap Acceptance: Gap acceptance in CORSIM is assigned by driver type based one type of movement. Decisions are based on current available gap and a personal gap acceptance value. In VISSIM gap acceptances are defined by the user and are specific to a particular location.
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COMPARISION OF MODELS TO ON-ROAD DATA This paper compares CORSIM and VISSIM for two different scenarios. The first scenario involved comparing speeds generated by the two models to speeds collected onroad. Two urban arterials in the Des Moines, Iowa metropolitan area were selected as test sites. The first location was University Avenue in the city of West Des Moines, which included a 1,240-foot section from 100th Street to 114th Street. The University Avenue section has 2-lanes of travel in each direction. Left turn lanes are present at the intersections with both 100th Street and 114th Street. The second location was 86th Street, which included a 2,640-foot section from Meredith Drive to Aurora Avenue in Urbandale. The 86th Street test section location also has 2-lanes of travel in each direction with left turn lanes at the intersections at Meredith Drive and Aurora Avenue. Both sections were fairly level. Two different studies were conducted. Since MOBILE bases emissions on average speeds, a floating car study was completed to compare average speeds on-road to those predicted by the two models. Average speed includes the total travel time along a link including idling. Therefore average speeds generated by simulation models depend on how well the model predicts queuing and delay. A spot speed study was also conducted to compare individual vehicle activity. Although this information cannot be directly used to estimate emissions with MOBILE, it does provide an indication of how well the simulation models are able to describe individual vehicle activity and could have impact for modal models in the future. Output from CORSIM and VISSIM were compared to on-road variables to evaluate model performance. Two analysis periods were used for comparison. Afternoon off-peak conditions (1:30 to 3:30 p.m.) and afternoon peak conditions (4:30 to 6:00 p.m.) were modeled. Data specific to each analysis period were collected individually. Volume and Roadway Data Collection A number of roadway characteristics are necessary to code a network in both CORSIM and VISSIM including roadway geometry, signal timing, and traffic characteristics. The number of lanes, location of stop bars, geometry of turning lanes, signal loop detectors, and signal head placement, were available from roadway design plans obtained from each of the respective cities. Base maps for the cities of Urbandale and West Des Moines were available from the Iowa Department of Transportation and were used to digitize the respective street networks. Turning movement counts were conducted concurrently with on-road speed studies. Volume counts were collected for both the intersection immediately upstream and downstream of each study location for both the off-peak and peak period. Volume counts included vehicle classification. The percentage of heavy-trucks was less than 1% at all locations. All intersections were modeled as pretimed and initial offsets calibrated to field observations of traffic behavior.
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7 Coding CORSIM and VISSIM Base maps were used to code the networks for the two study areas in CORSIM and VISSIM. The networks were coded into CORSIM using Synchro 3.2. All signals in the network were pretimed with signal times based on optimized plans provided from the Urbandale and West Des Moines engineering departments. Traffic volumes, turning movements, and vehicle classification were collected during the site study and were coded separately for each analysis period (peak and off-peak). Posted link speed limit was the average link speed used for CORSIM. VISSIM requires a distribution of speeds, which is difficult to collect without doing an extensive site study. To enter link speeds, the minimum and maximum speeds collected during the spot speed study were used as upper and lower bounds for the speed distribution. After coding each simulation model, test runs were conducted to evaluate model output and determine whether any errors were apparent. Once the modelers were satisfied, each model was run a number of times for each test section and each analysis period. Random seeds were used to vary simulation runs. Results were averaged across model runs. Average speed can be output both models by link. Mid-block speeds are not automatically output. VISSIM allows extraction of individual vehicle speeds by using vehicle detectors which were placed at 50-feet upstream and downstream of the midblock location. Four detectors were required to capture two-lanes of travel and to be able to cover the midblock area of the lanes (two upstream and two downstream). As vehicles in the simulation model travel over the detector, vehicle activity is recorded into user specified files. This allows either raw data for individual vehicles to be recorded and then be viewed by a text editor and imported to a spreadsheet. In CORSIM, a C program was used to extract vehicle speeds 50 meters upstream and downstream from the midblock of each location for a total of approximately 100 feet of lane length. This sample of vehicles that passed between these points was then output to a spreadsheet for analysis. Spot Speed Study Vehicle speed was collected on-road at the midblock location of each study section during the peak and off-peak periods. Speeds were collected using a laser-range finding devices (LRF). LRF are portable, tri-pod mounted devices, which measure the distance to an object at a high sampling frequency (238.4 distance measurements per second). The LRF is aimed at the rear of the vehicle and must lock onto the vehicle. Then the vehicle must be tracked for several hundred feet in order to calculate velocity. To randomize the data samples, every fifth vehicle that passed the data collection point was tracked during the off-peak period and every 10th vehicle was sampled during the peak hour period. Two data collectors were used to operate the LRF. One person tracked vehicles in the downstream direction with the LRF, while the other person recorded relevant information such as time and the type of vehicle sampled. Only passenger vehicles (cars, sport utility vehicles, passenger vans, and light-duty trucks) were tracked. Using distance data from the laser guns, velocity was calculated from the measured variables distance and time, providing a spot speed for each vehicle.
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A total of 174 vehicles were collected for the off-peak analysis period for 86th Street. For the peak period 89 vehicles were sampled. On the University Avenue section, 148 vehicles were collected during the off-peak period and 184 vehicles were collected during the peak period. To compare spot speeds generated by the simulation models against on-road spot speeds, a t-test was used to compare means and an F-test was used to compare variance. Spot speeds from each model for each section for each analysis period were compared against on-road speeds. An F-test was first performed to test variance. When results of the F-test indicated that the variances were significantly different, a separate variance estimate was used in the t-test. If variances were determined to be significantly different, the following variance estimate was used to perform the t-test:
(equation 1)
If the variances were not significantly different, a pooled variance estimate was used to perform the t-test given by:
(equation 2)
Once the variances were evaluated and the proper variance estimator calculated a t-test was performed to compare each combination according to:
(equation 3)
The null hypothesis was that the mean spot speeds were not significantly different. Tables 1 and 2 provide the results of the F-test and t-test. For the University section peak period, CORSIM’s average spot speeds were slightly lower but not significantly different from the spot speeds collected in the field. Average spot speeds reported by CORSIM were lower by 7.9 and 4.6 mph for the 86th Street off-peak and peak periods. The differences were statistically significant. Average speeds for the University off-peak period were 3.0 mph lower for CORSIM than field measurements and were also statistically significant. For all cases VISSIM spot speeds were statistically significantly lower than field spot speeds. For 86th Street the average spot speed was 3.4 and 2.2 mph lower and for University, speeds were 2.1 and 7.3 mph lower.
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9 Floating Car Studies An average speed study was also conducted. A test vehicle was used to drive each test routes and record total travel time for a number of runs. Data were also collected during the peak and off-peak periods. The floating car technique was used with the driver attempting to pass as many vehicles as passed the test vehicle according to the ITE Manual of Transportation Engineering Studies (19). Data were collected for both directions of travel (i.e. northbound and southbound). Average speed for both directions of travel were extracted from CORSIM and VISSIM output as well. A comparison of average speeds is presented in Table 3. As shown, CORSIM overpredicted northbound average speeds for both the peak and off-peak periods for 86th Street but underpredicted average speeds for both the southbound analysis periods. Average speeds were slightly higher in CORSIM for all scenarios for University Avenue. With VISSIM average speeds were within 1.5 mph of the field average speeds for both analysis periods of 86th Northbound and for the off-peak analysis period for University eastbound and for the peak analysis period for University westbound. For the two analysis periods for 86th Southbound, average speeds were underpredicted by 6.1 and 4.1 mph. Speeds were overpredicted by 4.9 mph for the off-peak period for University Avenue westbound and underpredicted by 5.3 mph for the University eastbound peak period. No systematic bias was noted for either simulation model. Comparing Differences in Emissions Between Modeled and On-Road Speeds The recently released emission rate model MOBILE6 estimates average, in-use fleet emission factors for VOC, CO, and NOx. Twenty-eight individual vehicle types are modeled including gas, diesel, and natural gas fueled passenger vehicles, heavy trucks, buses, and motorcycles for calendar years 1952 to 2050. Emissions can be modeled at different average speeds from 2.5 to 65 mph. MOBILE6 models emissions for four roadway categories including freeways, arterials, local roads, and freeway on- and offramps (1). To demonstrate differences in modeling that would result from the two simulation models as compared to actual on-road data, a MOBILE6 run was completed for each average speed represented by the field data and simulation models. An ambient temperature of 75º to 92º F was used. The scenario date was January 2002. Only arterials were considered. The default passenger vehicle composition was used to model passenger vehicles. Emission rates were calculated for a vehicle fleet that was 99% passenger vehicles and 1% heavy trucks diesel trucks to reflect the on-road fleet. All other model parameters were MOBILE6 defaults. MOBILE outputs emission rates in grams per mile. Total VOC and NOx emissions were calculated by multiplying the emission rate by segment length, vehicle volume for the 2-hour study period, and number of days per year. Emissions were modeled only for the normal business weekdays and not weekends. Results are presented in Figures 1 and 2. As shown, VOC and NOx emissions are not signicantly different for any scenario except for University westbound off-peak and the University eastbound peak analysis periods. Differences in emission are less than 7%.
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COMPARISON OF MODELS FOR PRIORITIZING CMAQ PROJECTS The Chicago Area Transportation Study (CATS) is the Metropolitan Planning Organization (MPO) for northeastern Illinois. The organization is responsible for administering the CMAQ program to northeastern Illinois each year. Currently, CORSIM is used to determine differences in average speeds between the pre-project and post-project state of Intersection Improvement and Bottleneck Elimination projects. Average speeds are modeled in CORSIM and then combined with emission rates from MOBILE5 to determine reductions in emissions from each project. Emissions reduced are divided by total project cost and then each project ranked. Projects with the highest emissions reduced to cost ratio are selected for CMAQ funding. As part of this study, data for several intersections originally modeled in CORSIM for the CMAQ program were obtained from CATS. Each intersection was coded and modeled in VISSIM as well by the researchers. Averages speeds from each model were compared for the pre and post-project stages to estimate differences between the two. CATS Methodology For the project ranking process, it is crucial that the average speeds of the preproject and post-project are accurate representations of the existing conditions of the study area. In the CATS methodology quarter mile sections of road on each intersection approach are analyzed. The analysis period for both the pre-project state and post-project state were the evening peak hour volumes. An analysis period of 15 minutes is used to obtain the average project speed. The Chicago Area Transportation Study requires that a municipality submitting a project proposal to be evaluated for CMAQ funding, completes an Input Module worksheet from the Highway Capacity Manual that contains the most accurate data possible for both the pre-project state and the post-project state. This worksheet provides the basic intersection data necessary to simulate an intersection. The pre-project state is the current intersection configuration including signal timing, lanes, lane channelizations, and current traffic volumes. The post-project state is the intersection configuration after there has been a change in the signal timings, or number of lanes and lane channelizations. The traffic volumes remain constant. A majority of projects change all three parameters. Research Methodology Data for three intersections were provided by CATS. This consisted of an aerial image and HCM Input worksheet for each intersection. CORSIM data that was used to evaluate the intersections was also provided. Each intersection is described in the following sections. Wise Road at Springinsguth Road: The intersection is located in Schaumburg, Illinois. The current configuration has a traffic control signal with a 60-second pretimed cycle
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11 length. Each approach on Springinsguth Road has a left turn lane, and a shared through/right lane. Each approach on Wise Road has a left turn lane, a through lane, and a shared through/right lane. The project proposal includes a new signal phase configuration with a 60-second pretimed cycle length. The new lane configuration for the northbound Springinsguth Road approach includes a left turn lane, a through lane, and a shared through/right lane. The new lane configuration for the southbound Springinsguth Road approach includes a left turn lane, a through lane, and a right turn lane. Both signal timings in this project allow for permitted left turns. Randall Road at IL 64: This intersection is located in St. Charles, Illinois. The current configuration has a traffic control signal with a 150-second pretimed cycle length. Each approach has one left turn lane, one through lane, and a shared through/right lane. The proposed design includes a new signal phase configuration with a 90-second pretimed cycle length. The new lane configuration for each approach on Randall Road will have two left-turn lanes, three through lanes, and a right turn-lane. The new lane configuration for each approach on IL 64 will have two left-turn lanes, two through lanes, and a rightturn lane. Cemetery Road at Washington Street: This intersection is located in Gurnee, Illinois. The current configuration has a traffic control signal with a 90-second pretimed cycle length. The southbound Cemetery Road approach has one lane for all movements. The northbound Cemetery Road approach has a left turn lane, and a shared through/right lane. The eastbound Washington Street has a left turn lane, a through lane, and a right turn lane. The westbound Washington Street has a left turn lane, and a shared through/right lane. The proposed design includes a new signal phase configuration with a 90-second pretimed cycle length. The new lane configuration for each approach on Cemetery Road will have a left turn lane, and a shared through/right lane. The new lane configuration for each approach on Washington Street will have a left turn lane, a through lane, and a shared through/right lane. Both signal timings in this project allow for permitted left turns. Using intersection configuration information from the aerial photographs and other traffic parameters from the worksheet, each intersection was coded into VISSIM. VISSIM does require additional information such as distribution of vehicle speeds by link. However when data beyond that which was available from the images or worksheet was necessary, default values from the program were used. Simulation models do perform better when calibrated but it was desired that only the actual information that would already have provided to CATS staff for CMAQ project evaluation be used since this was the information used to code CORSIM and it was desired that the parameters for coding CORSIM and VISSIM be the same as much as possible. Average speeds were available for the pre- and post-project stages for each intersection in CORSIM. Average speeds for the pre- and post-project stages were also extracted from VISSIM output.
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12 Results Average speed results are presented in Table 5. There is a distinct difference in the average speeds from VISSIM and CORSIM. The overall VISSIM average speeds were 11% greater than the CORSIM average speeds. VISSIM average speeds for the pre-project condition were 25% greater than the CORSIM average speeds. VISSIM average speeds for the post-project condition were 3% less than the CORSIM average speeds. As noted in the pre-project stage, VISSIM estimates higher average speeds than CORSIM. In the post-project stage, VISSIM modeled lower average speeds for two intersections and slightly higher average speeds for the third. Overall, changes in average speeds due to anticipated project improvements were lower in VISSIM than for CORSIM. Although it could not be quantified with the data from the study, it appears that VISSIM may be less sensitive to changes in model parameters than CORSIM. Emission Reduction Emission rates used by CATS for VOC and NOx were used to compare differences in emissions reductions for the pre- and post-project states using the results provide by CORSIM and VISSIM. A table of emission rates by average speed originally created by MOBILE5 were used as look up tables. Emission rates in grams per mile were multiplied by the study section length, link volume for a 2-hour study period, study days per year (included business days—Monday through Friday) yielding a total quantity of pollutant in kg as shown in Table 5. Emission reductions due to project improvements were also calculated. As shown, for all three projects emission reductions using CORSIM were greater than for VISSIM. SUMMARY CORSIM and VISSIM were compared in calculating average speeds for two different scenarios and differences. In the first scenario, the models were compared to spot speeds and average speeds collected in the field for two arterials for both afternoon off-peak and evening peak periods. Both models underpredicted mid-block spot speeds. Results were mixed for average speeds. Both overpredicted speeds in several instances and underpredicted speeds in others. For several situations, the models predicted averages speeds that were within 1.5 mph of the average speeds from a floating car study. VOC and NOx emissions were calculated for the different situations and overall emissions predicted by results from CORSIM, VISSIM, and the field studies were less than 7%. In the second scenario, average speeds for three CMAQ projects were estimated by CORSIM and VISSIM and compared. Speeds and resulting emissions were calculated for existing conditions and for signalized intersection improvements. CORSIM predicted greater differences in pre- and post-project speeds for all three projects resulting in greater reductions in emissions. No systematic biases were noted in either model. Differences in average and spot speeds are highly dependent on model input. Therefore model coding and validation are important. It is suggested that either model may perform adequately for estimating average speeds as input to project-level emissions analysis. However, proper validation is important.
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REFERENCES 1. USEPA Air and Radiation. User’s Guide to MOBILE6.0: Mobile Source Emission Factor Model. January 2002. EPA420-R-02-001. 2. Cicero-Fernández, P. And Long, J. R. 1994. Modal acceleration testing on current technology vehicles. The Emission Inventory: Perception and Reality, Air and Waste Management Association, Pittsburgh, PA. pp. 506–522. 3. LeBlanc, D. C., Saunders, M., Meyer, M. D., Guensler, R. 1995. Driving pattern variability and impacts on vehicle carbon monoxide emissions. TRR 1472. National Research Council. pp. 45–52. 4. Yu, L. 1998. Remote vehicle exhaust emission sensing for traffic simulation and optimization models. Transportation Research D. Vol. 3, No. 5. pp. 337–347. 5. Frey et al, 2000. H. Christopher Frey, Nagui Rouphail, Aler Unal, and James Colyar. Emissions and Traffic Control: An Empirical Approach. Proceedings of the CRC OnRoad Vehicle Emissions Workshop. March 2000. San Diego, CA. 6. FHWA, 1999. The Congestion Mitigation and Air Quality Improvement Program (CMAQ) Under the Transportation Equity Act for the 21st Century: Program Guidance. Federal Highway Administration. April 1999. 7. Middleton, Mark D. and Scott A. Cooner. Evaluation of Simulation Models for Congested Dallas Freeways. Texas Transportation Institute. Report 3943-1. November 1999. 8. Cambridge Systematics, 2000. A Sampling of Emissions Analysis Techniques for Transportation Control Measures: Final Report. Prepared for FHWA. October 2000. 9. Euritt, Mark A., Jiefeng Qin, Jaroon Meesomboon, and C. Michael Walton. 1994. “Framework for Evaluating Transportation Control Measures: Mobility, Air Quality, and Energy Consumption Trade-Offs.” TRR 1444. National Research Council. pp. 135144. 10. Chundury, Sastry and Brian Wolshon. 2000. “Evaluation of CORSIM CarFollowing Model by Using Global Positioning System Field Data.” TRR 1710. National Research Council. pp. 114-121. 11. Prevedouros, Panos D. and Yuhao Wang. 1999. “Simulation of Large Freeway and Arterial Network with CORSIM, INTEGRATION, and WATSim.” TRR 1678. National Research Council. pp. 197-207.
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14 12. Mystkowski, Cindy and Sarosh Khan. 1999. “Estimating Queue Lengths by Using SIGNAL94, SYNCHRO3, TRANSYT-7F, PASSER II-90, and CORSIM.” TRR 1683. National Research Council. pp. 110-117. 13. Dijker, Thomas, Piet H. L. Bovy, and Raymond G. M. M Vermijs. Car-Following Under Congested Conditions. TRR 1664. National Research Council. 1998. pp. 20-28. 14. Zhang, Yunlong, Larry E. Owen, and James E. Clark. Multiregime Approach for Microscopic Traffic Simulation. TRR 1644. National Research Council. 1998. pp. 103115. 15. Wang, Yuhao and Panos D. Prevedouros. Comparison of INTEGRATION, CORSIM, and WATSim in Replicating Speeds on Three Small Networks. TRR 1644. National Research Council. 1998. pp. 180-92. 16. Hallmark, Shauna L. and Randall Guensler. Comparison of Speed-Acceleration Profiles from Field Data with NETSIM Output for Modal Air Quality Analysis of Signalized Intersections. TRR 1664. National Research Council. 1998. pp. 40-46. 17. PTV, 2000. VISSIM 3.50 User Manual. PTV Planung Transport Verkehr AG. December 2000. Karsruhe, Germany. 18. Bloomberg, Loren and Jim Dale. 2000. “Comparison of VISSIM and CORSIM Traffic Simulation Models on a Congested Network.” TRR 1727. National Research Council pp. 52-60. 19. Robertson, H. Douglas, Joseph E. Hummer, and Donna C. Nelson. 2000. Manual of Transportation Engineering Studies. Institute of Transportation Engineers. Washington DC.
TRB 2003 Annual Meeting CD-ROM
Original paper submittal – not revised by author.
15 TABLES Table 1: Spot Speed Results for 86th Street 86th Off-Peak CORSIM VISSIM FIELD Mean (mph) 30 34.6 37.9 Std. Dev. 4.4 4 3.9 f-Statistic 5.19 .611 N/A f-Significance .023 .435 N/A t-Statistic -17.68 -7.87 N/A t-Significance p
