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Optimizing Traffic Control to Reduce Fuel Consumption and Vehicular Emissions


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tions and frequent stops at intersections. However, low traffic and continuous progression along streets do not guarantee the lowest fuel consumption and emissions. Excessive speeding, which may occur on roads with low traffic, may cause increased emissions for several pol-lutants. The best flow of traffic on arterial streets, in terms of fuel con-sumption and emissions, is the one with the fewest stops, shortest delays, and moderate speeds maintained throughout the commute (1). One of the ways to reduce excessive stop-and-go driving on urban streets is to optimize signal timings. Historically, signal timing opti-mization tools were developed to reduce delays and stops experienced by urban drivers. The concept of optimizing signal timings to reduce fuel consumption and emissions was first addressed by Robertson et al. (2). However, at that time traffic was simulated by macroscopic and analytical tools, and individual driving behavior was not sidered. Similarly, the relationship between traffic activity, fuel con-sumption, and vehicular emissions, which was applied to all vehicles, was a simplistic and linear relationship (2).

In recent years powerful tools for traffic modeling, fuel consump-tion, and emissions modeling have been developed. Microscopic simulation tools, such as VISSIM, have been used for more than a decade to model individual traffic behavior (3). Similarly, emissions models, such as the comprehensive modal emission model (CMEM), were developed to estimate second-by-second emissions of individ-ual vehicles based on modes of a common driving cycle (4). These two types of microscopic models were coupled to estimate instanta-neous emissions based on second-by-second activities of individually behaved vehicles (5–7).

However, signal timing optimization models have been developed that now use microscopic traffic models to evaluate and improve the quality of signal timings (8, 9). Researchers have reported that these new signal optimization tools generate signal timings that reduce delays and stops when compared with the ones generated by macro-scopic optimization tools (10). However, no research has been per-formed that integrates all these new microscopic tools in order to find the best signal timings that would minimize fuel consumption and emissions. The research reported here aims to fill that gap in existing practice by integrating a microscopic traffic simulator, a comprehen-sive microscopic emission estimation model, and a stochastic sig-nal optimization tool to provide sigsig-nal timings that minimize fuel consumption and vehicular emissions.


In previous decades, many researchers have evaluated the effects of traffic signal timings on the environment (11–18). Effects are eval-uated through an investigation of the amount of fuel consumption

Optimizing Traffic Control to Reduce Fuel

Consumption and Vehicular Emissions

Integrated Approach with VISSIM, CMEM, and VISGAOST

Aleksandar Stevanovic, Jelka Stevanovic, Kai Zhang, and Stuart Batterman

105 One way to reduce excessive fuel consumption and vehicular emissions on urban streets is to optimize signal timings. Historically, signal timing opti-mization tools were used to reduce traffic delay and stops. The concept of optimizing signal timings to reduce fuel consumption and emissions was addressed decades ago with tools that are now considered outdated. This study advocates a fresh approach to integrating existing state-of-the-art tools for reassessing and ultimately minimizing fuel consumption and emissions. VISSIM, CMEM, and VISGAOST were linked to optimize signal timings and minimize fuel consumption and CO2emissions. As

a case study, a 14-intersection network in Park City, Utah, was used. Signal timings were optimized for seven optimization objective functions to find the lowest fuel consumption and CO2emissions. Findings show

that a formula commonly used to estimate fuel consumption in traffic simulation tools inadequately estimates fuel consumption and cannot be used as a reliable objective function in signal timing optimizations. Some of the performance measures used as objective functions in the opti-mization process were proved to be ineffective. When CMEM-estimated fuel consumption is used as an objective function, estimated fuel savings are around 1.5%, a statistically significant decrease. Further research is needed to find an effective way to minimize fuel consumption and emissions by using the proposed approach.

Both continuous transportation growth in the Western world and the recent economic boom in India, China, and many third-world coun-tries have had a tremendous impact on the use of fossil fuels. The increase in fuel consumption affects the environment (the greenhouse effect), health (air pollutants), and the economy (increased fuel prices). Increased fuel consumption is mainly caused by two factors. First, millions of new drivers start using private cars as a main mode of transportation every year. Second, when these new travelers join existing traffic demand, traffic congestion increases because highway capacity does not increase commensurately with the new demand.

The highest fuel consumption on urban arterials is associated with driving in congested traffic, characterized by higher speed fluctua-A. Stevanovic and J. Stevanovic, Department of Civil and Environmental Engineer-ing, University of Utah, 122 South Central Campus Drive, Room 104, Salt Lake City, Utah 84112-0561. Current affiliation for A. Stevanovic, Department of Civil Engineering, Florida Atlantic University, 777 Glades Road, Building 36, Room 231, Boca Raton, FL 33431. Current affiliation for J. Stevanovic: 2145 Northwest Third Court, Boca Raton, FL 33431. K. Zhang and S. Batterman, Environmental Health Sciences, School of Public Health, University of Michigan, 1420 Washington Heights, Room 6037, Ann Arbor, MI 48109-2029. Corresponding author: A. Stevanovic, aleks.stevanovic@fau.edu.

Transportation Research Record: Journal of the Transportation Research Board, No. 2128,Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 105–113.


and vehicle emissions (both air pollutants and greenhouse gases) for various traffic conditions by using various methods and tools. Both field and theoretical tests have shown that optimized signal timings decrease fuel consumption and vehicle emissions compared with nonoptimized timings.

Robertson et al. optimized signal timings by using TRANSYT 8 to minimize fuel consumption (2). They found that when signal timings are not optimized to reduce delays but to reduce total fuel tion, the benefits of such signal timings may decrease fuel consump-tion by up to 3%. The fuel consumpconsump-tion was estimated from its linear relationship with traffic performance measures (delay, stops, and average speed) (2). The research set an industry standard in optimiza-tion of signal timings by defining a performance index (PI) as a linear combination of delay and stops that should be minimized to get min-imal fuel consumption. Experiments showed that each stop should be associated with a penalty delay of 20 s if fuel consumption is going to be minimized. This PI became a standard objective function for opti-mizing signal timings, and the defined weights for delay and stops have not changed significantly since then.

To estimate air pollutant concentrations, Park et al. coupled the VISSIM microsimulator model with MODEM, an emissions inven-tory database (19). Concentrations estimated by using a Gaussian dis-persion model were comparable with those estimated from another macroscopic model but slightly different from levels measured in the field.

Instead of using an emissions inventory database, Nam et al. coupled VISSIM with CMEM to estimate emissions from a single vehicle (5). The comparison with the field measurements found that CMEM is acceptable when capturing aggregated hydrocarbon (HC) and carbon monoxide (CO) trends but less accurate for carbon diox-ide (CO2) and nitrogen oxides (NOx). An integrated VISSIM–CMEM model was also used to show that the signal timings, optimized for progression in TRANSYT 9, significantly reduced pollutant emis-sions and fuel consumption on an arterial road (6). Oda et al. developed a simulator to estimate CO2emissions (20). They used a macroscopic traffic flow model to input traffic activities into the CO2 simulator. The authors wanted to optimize traffic control settings to reduce CO2emissions. However, because of the huge computational burden needed to estimate CO2for all vehicles in the network, the authors simplified the experiments. Instead of minimizing CO2they minimized the number of stops, which they had shown was highly correlated with CO2(20). Another integrated VISSIM–CMEM model was used to show that a scenario with optimal traffic control reduced various pollutant emissions (CO, HC, NOx) from 3% to 15%. The research was done for a road network in Beijing by Chen and Yu (7). Qu et al. investigated impacts of reduced freeway speed limits on traffic emissions in Houston, Texas (21). The authors used TRANSIM to model traffic. The traffic activities were imported into three emis-sion models: TRANSIMS (CMEM), MOBILE 5, and MOBILE 6. Emissions of three major pollutants [volatile organic compounds (VOC), NOx, and CO] were modeled in each of the three emissions models to investigate the effectiveness of freeway speed limit reduc-tions as a way to decrease emissions. The results were mixed, show-ing that some models justify the reduction of speed limits while others do not. The study also showed TRANSIMS’s inability to model changes in speed limits accurately because of its discrete approach in modeling vehicular speeds.

Another attempt to determine signal timings that minimize fuel consumption and vehicular emissions was reported by Smith et al. (22), who briefly addressed SCOOT operations that minimize vehicle emissions. Traditionally, SCOOT has been used to minimize delays and stops in traffic by adjusting signal timings based on traffic demand

measured in real time. The authors tested a new version of SCOOT that can minimize any of the five emission pollutants—CO, CO2, VOC, NOx, and PM10—instead of the traditional PI. The pollutants were estimated on the basis of the SCOOT traffic model. The authors used a new SCOOT feature to minimize emissions by adjusting traf-fic control settings for the U.K. region of Leicester. The results showed that emissions for any of the pollutants could be reduced by up to 2% if an emission-related objective function is used during SCOOT optimizations. Unfortunately, these reductions were not statistically significant at the 95% confidence level. A major limitation of the approach was the fact that SCOOT’s mesoscopic traffic model was not capable of modeling second-by-second modular operations (accel-eration, cruising, idling) of individual vehicles. Rather, SCOOT bases its emission estimates on average emission rates for each vehicle class (four classes are available), and traffic flow and speed estimates are averaged over each link (13, 23).

In summary, researchers have used various traffic simulation tools and various methods to estimate fuel consumption and vehicle emis-sions. Most applications have shown that optimized signal timings decrease fuel consumption or vehicle emissions or both but are based on macroscopic or mesoscopic models and unreliable objective func-tions. However, no research has addressed the optimization of sig-nal timings based on evaluations of single-vehicle emissions and driving behavior. Further, without an objective function related to accurate fuel consumption and emission estimates, signal timings can-not be optimized to minimize these environmental impacts. Research presented here optimizes signal timings on the basis of CMEM emis-sions estimates for a population of vehicles whose individual driving behaviors were modeled in VISSIM. Optimization was used to minimize fuel consumption and CO2emissions.


VISSIM is a microscopic, time-step and behavior-based model devel-oped to simulate urban traffic and public transport operations. The program can analyze vehicle operations under different lane config-urations, traffic composition, traffic signals, and public transport stops. This ability makes it a useful tool to evaluate traffic in alternative networks and to develop transportation engineering and planning measures of effectiveness (3).

The accuracy of a traffic simulation model is mainly dependent on the quality of the vehicle modeling, such as the methodology of moving vehicles through the network. In contrast to less complex models that use constant speeds and deterministic car-following logic, VISSIM uses the psychophysical driver behavior model developed by Wiedemann (3).

VISSIM has several ways of modeling traffic control. One of the most popular ways is the emulation of the industry standards in traf-fic control established by the National Electrical Manufacturers Asso-ciation (NEMA). Recent experiments showed that signal timings generated by VISSIM’s NEMA emulator do not differ practically from those generated by real-world controllers.

CMEM Model

CMEM is a physically based, power-demand model developed by the University of California at Riverside, the University of Michigan, and Lawrence Berkeley National Laboratory (4). After a variety of


enhancements, the latest version (3.0) includes submodels for light-duty vehicles (LDVs) and heavy-light-duty diesel (HDD) vehicles. These submodels estimate vehicle tailpipe emissions (CO, HC, NOx, and CO2) in different modes of vehicle operation, such as idling, cruising, acceleration, and deceleration. Scora and Barth suggested that tem-poral and vehicular aggregations were necessary in practice because CMEM was developed to predict emissions for vehicle categories (4). The temporal scale ranges from second-by-second, several seconds (mode) to driving cycle or scenario, and the vehicular scale ranges from a specific vehicle, vehicle technology category, to general vehicle mix or fleet.

CMEM model inputs include traffic composition, vehicle and oper-ation variables (e.g., speed, acceleroper-ation, and road grade), and model-calibrated parameters (e.g., cold start coefficients and an engine friction factor) (4). Outputs are tailpipe emissions and fuel consump-tion. Emissions (in grams per second) are predicted as the product of fuel rate (FR, in grams per second), engine-out emission indices (grams of emission per grams of fuel), and time-dependent cata-lyst pass fraction (CPF), defined as the ratio of tailpipe to engine-out emissions. CPF is mainly affected by the fuel-to-air ratio and engine-out emissions.

LDV and HDD models have similar structures (4). Both are com-posed of six modules: engine power demand, engine speed, fuel-to-air ratio for the LDV model or engine control unit for the HDD model, fuel rate, engine-out emissions, and CPF for the LDV model or after-treatment pass fraction for the HDD model. Key parameters (e.g., vehicle mass, engine size, fuel type) depend on vehicle technology, fuel delivery system, emission control technology, vehicle age, and other factors. CMEM has been calibrated by using data from the National Cooperative Highway Research Program, which includes both engine-out and tailpipe emissions of CO, HC, NOx, and CO2 for over 400 vehicles in 36 vehicle technology categories.


VISGAOST is an optimization program for signal timings of traffic controllers based on their performance in VISSIM microscopic sim-ulation. The program bases its optimization on the stochastic nature of genetic algorithms (GAs). The general structure of VISGAOST GA optimization is well documented (10). The basic version of VISGAOST is written in C++and relies on VISSIM’s input and out-put files (3). The key part of the program is a simple GA similar to other GAs used for signal timing optimization (24).

The first version of VISGAOST enabled the optimization of all four basic signal settings: cycle, offset, split, and phase sequence. The program was tested and evaluated for the network in Park City, Utah, consisting of three groups of coordinated intersections and two actuated intersections. Results confirmed that VISGAOST can find timing plans that work better in VISSIM than the initial timing plans from the field (9). Further, the results showed that the GA-optimized plan was better than the timing plan generated by the traditional optimization tool SYNCHRO.

VISGAOST application was extended to enable optimization of transit signal priority (TSP) settings. The two most common TSP settings—green extension and early green—were optimized for a corridor of seven signalized intersections in Albany, New York. Results showed that the optimized timing plan improved overall traffic performance and reduced person delay (10).

The extended version of VISGAOST, presented in this paper, enables optimization of signal settings to minimize fuel consumption

and vehicular emissions estimated by CMEM. The program has been modified to accommodate new linkage to CMEM and some new esti-mates from VISSIM. The steps below describe the basic operations in the VISGAOST optimization process.

Step 0:Initializing

G,total number of generations;

T,total number of timing plans per generation;

⑀, convergence threshold; i,current number of population; i=0.

Generation of initial population pi

of timing plans tpkk∈ [1, . . . , T]

• Read field timing plan tp1

from database, • Generate tpk

k∈[2, . . . , T].

Step 1:Evaluating population Evaluation of tpk pi k∈[1, . . . , T] • Write tpk to database, • Simulate tpk,

• Estimate emissions for tpk, • Calculate fitnessk


Step 2:Testing termination criteria

• Find b, fitnessbfor which fitnessb=max(fitness1, . . . , fitnessT); • Find fitnessafor which

fitnessa=average( fitness1, . . . , fitnessT ); • Test rule. IF ((i=G) OR ((fitnessbfitnessa) < ))

Stop and RETURN tpbpi ELSE

GO TO Step 3

Step 3:Generating new population i=i+1

Generation of new population pi

• Select best-ranking timing plans from pi−1 , • Generate pi

through GA operations. GO TO Step 1


Figure 1 shows the integration of VISSIM, CMEM, and VISGAOST to find signal timings that reduce fuel consumption and vehicular emissions. The optimization process starts with the VISGAOST gen-eration of the initial population of signal timings, which is seeded by the existing set of signal timings from the field. Each generated sig-nal timing plan is evaluated in VISSIM. As a result of the evalua-tion process, VISSIM outputs a vehicle record file with relevant second-by-second data for each vehicle in the network for the entire simulation period.

The vehicle record file is processed by the VISSIM–CMEM inter-face and sent to CMEM. CMEM estimates emissions and fuel con-sumed during the evaluation of that particular signal timing plan. The CMEM estimates are then summed for all vehicles in the network during the entire simulation period.

VISGAOST receives the summed fuel consumption (or vehicular emissions) for each signal timing plan from the current population. A signal timing plan with the lowest fuel consumption (emissions) will be selected as the best one and saved to be compared with the best one from the next generation. Then the GA procedure within VISGAOST uses four basic GA operators to create a new popula-tion of signal timings. The whole process is repeated until one of two predefined termination criteria is met.



Connections between traffic microsimulation tools, such as VISSIM, and instantaneous emission models, such as CMEM, have been described elsewhere (5–7), and other studies provide more informa-tion about the VISGAOST and VISSIM interface (9, 10). Here the focus is on specific modifications of these two interfaces that enable functional communication among the three tools.

The major limitations of several previous attempts to integrate traf-fic simulation and emissions estimation models were that they were not applicable to U.S. traffic conditions because the emissions were based on European vehicles (15, 25, 26). In other studies, heavy vehi-cles were not modeled directly because there was no HDD model in CMEM at that time (5, 27–29). Finally, in several studies vehicle emissions were overestimated because low-emitting vehicles were improperly represented in the old CMEM version (4). This study improves the emissions modeling approach by using a recent version of the CMEM software (3.0) and a representative sample of vehicles used in the United States.

A program built in Java connects VISSIM with the LDV and HDD models in CMEM (Figure 1). The program’s logic is similar to that described previously (6, 28). The program improves on the previous developments by modeling diesel trucks directly, calling either LDV or HDD core models for each individual vehicle (instead of the LDV batch model, which limits the number of records and vehicles that can be handled) (4), and using Java, a platform-independent language.

For each vehicle VISSIM provides simulation time, a vehicle iden-tifier, a vehicle type (LDV or truck), speed, and acceleration or decel-eration on a second-by-second basis. The Java interface program imports the VISSIM output file to CMEM, which uses individual vehicle data to estimate instantaneous emissions for each vehicle. Each VISSIM vehicle type is assigned (by the Java program) to a CMEM vehicle category.

The assignment of vehicle categories follows the mapping process described in Table 1, which maps the vehicle types from MOBILE 6.2 to the CMEM vehicle categories. It was assumed that the simulated vehicle fleet is composed of the light-duty gasoline vehicles (LDGVs) and heavy-duty diesel vehicles (HDDVs) defined in MOBILE 6.2.


Simulation time: 600 to 4200

Parameter Value Total travel time[h] 835.8 Total delay time[h] 159.2 Number of stops 21828 Stopped delay[h] 84.2 Network Performance VISSIM Output Split[1,8]=[[10.0,23.0,10.0, 23.0,10.0,23.0,10.0,23.0]]; LeadPhase[1,8]=[[1,0,0,1,1, 0,1,0]]; CycleLength[1]=[66.0]; Offset[1]=[30.0]; VISSIM Input SignalGroups[8] =[1.0,2.0,3.0,4.0 ,5.0,6.0,7.0,8.0];

Optimized Signal Settings

Measures of Effectiveness t; Veh; Type; v; a; 1.0; 2; 1001; 23.18; 0.86 ; 1.0; 1; 1001; 25.75; 0.69 ; 1.0; 3; 1001; 24.55; 0.82 ; 2.0; 5; 1001; 23.80; 0.59 ; 2.0; 4; 1001; 24.60; 0.80 ; 2.0; 2; 1001; 23.76; 0.86 ; Ve hi cl e Reco rd VISSIM Output Distance Traveled 0.55 mi Fuel Use 17.0585 (grams/mile) CO2 = 17.8462 (grams) CO = 7.5225 (grams) HC = 0.0994 (grams) NOx = 0.0931 (grams) Control File: Activity File:

veh-CMEM Output VISSIM-CMEM-VISGAOST Interface CO2 1.3416976E7 CO 1459375.8 HC 25465.648 NOx 29045.52 Fuel 3147279.0 Dist 20699.896 Summed Estimations Distance Tr aveled 0.55 mi Fuel Use 17.0585 (grams/mil e) CO2 = 17.8462 (grams) CO = 7.5225 (grams) HC = 0.0994 (grams) NO x = 0.0931 (grams) Control Fi le: Activity Fi le:

veh-Di stance Tr aveled 0.15 mi Fuel Use 10.0585 (grams/mile) CO2 = 17.8462 (grams) CO = 7.5225 (grams) HC = 0.0994 (grams) NOx = 0.0931 (grams) Control File: Ac tivi ty Fi le:

veh-Distance Traveled 0.35 mi Fuel Use 13.1246 (grams/mile) CO2 = 22 .22 (grams) CO = 2.33 5 (grams) HC = 0.07 91 (grams) NO x = 0.0592 (grams) Control File: Activity File:

veh-Distance Traveled 0.35 mi Fuel Use 13.1246 (grams/mile) CO2 = 22.22 (grams) CO = 2.335 (grams) HC = 0.0791 (grams) NOx = 0.0592 (grams) Control File: Activity File:

veh-Type CMEM Description MEM Code Percent

ULEV 51 0.08

PZEV 52 0.08

Tier 1 < 50k, low ratio 10 0.09 Tier 1 < 50k, high ratio 11

8 9

0.09 Tier 1 > 50k, low ratio 0.20 Tier 1 > 50k, high ratio 0.20 3-way catalyst, FI, > 50k miles low 4 0.13 3-way catalyst, FI, > 50k miles high 5 0.13 HDDV 1999-2002, 4 stroke, Elect 47 1.00 LDGV Lookup Table OR LDV HDD CMEM


It was also assumed that LDGVs can be represented in CMEM by two Tier 2 vehicle categories, ultra-low-emitting vehicles (ULEVs) and partial-zero-emitting vehicles (PZEVs); four Tier 1 vehicle cat-egories; and two categories of old vehicles (Table 1). The LDGV category was matched to these eight CMEM categories according to the vehicle age distribution from MOBILE 6.2 (30) and the Tier 2 phase-in schedule (31). CMEM does not include HDDVs after

2002, and thus the CMEM category of 1998–2002 HDDVs was cho-sen instead. Trucks manufactured before 1998 or after 2002 were not considered in the study.

Many vehicle types can be defined in VISSIM; however, the ini-tial experiments were constrained to two: passenger cars and heavy vehicles (trucks). Depending on the VISSIM vehicle type, the Java program utilizes either the CMEM LDV model or the CMEM HDD model. A CMEM model (LDV or HDD) computes fuel consumption and vehicular emissions for each vehicle in the simulation outputs. The Java program summarizes individual vehicles’ fuel consumption and emissions (CO, HC, NOx, and CO2) to obtain the total values for the entire road network.


Study Network, Park City, Utah

To optimize signal timings for minimal fuel consumption, the Park City road network, located in Utah near Salt Lake City, was chosen. The network consists of two suburban arterials, SR-224 and SR-248, and many crossroads. The network, shown in Figure 2, has 14 sig-nalized intersections and average annual daily traffic of 32,000 and 20,000 on SR-224 and SR-248, respectively.

0 0.5 1 km

FIGURE 2 VISSIM model of road network in Park City, Utah. TABLE 1 Mapping of Vehicle Categories in MOBILE 6.2 and CMEM


Type CMEM Description Code Percent

LDGV ULEV 51 0.08

PZEV 52 0.08

Tier 1 < 50k, low ratio 10 0.09 Tier 1 < 50k, high ratio 11 0.09 Tier 1 > 50k, low ratio 8 0.20 Tier 1 > 50k, high ratio 9 0.20 3-way catalyst, FI, > 50k miles low 4 0.13 3-way catalyst, FI, > 50k miles high 5 0.13 HDDV 1999–2002, 4-stroke, electric 47 1.00 NOTE: FI =fuel injected.


VISSIM Model of Park City Network

Building, calibrating, and validating the VISSIM model required extensive field data collection and data reduction efforts. A team of 10 students was employed and trained to collect various traffic data during three weeks in August 2005. All data used in this study were collected between 4:00 and 6:00 p.m. on workdays under fair weather and dry pavement conditions. The following data were collected: turning-movement counts, saturation flow rates, stopped delay at the intersections, spot speed data, corridor vehicle classification counts, and corridor travel times. On the basis of the collected data the fol-lowing parameters were adjusted to calibrate the VISSIM model: traf-fic inputs and routing decisions, the two car-following parameters in the Wiedemann 74 VISSIM model, control delays, desired speed decisions, and vehicle compositions. Validation of the model was done with the corridor travel times. All of the segment travel times from the field and VISSIM were close, but four of them (two in each direction) were still statistically different (two-tailed t-test was per-formed, with a=0.05 and n=15). A detailed description of the data, calibration process, and validation results was given elsewhere (9).

Field Signal Timings

The field signal timings were implemented under mixed actuated-coordinated and actuated-unactuated-coordinated control. Intersections of Bonanza Drive and SR-248 and SR-248 and Comstock Drive are actuated-uncoordinated, and all others are coordinated. The first three intersections in the Kimball Junction area were run on 128-s cycles. The other intersections all ran on 106-s cycles with exception of Deer Valley and Bonanza Drive, which used double cycling. The signal timings in the field were monitored regularly, but there were no recent major updates. Traffic engineers maintained the signal timings to achieve good progression between intersections, which was reflected in the initial signal timings in the optimization.

VISGAOST Optimizations

There were two major objectives for the VISGAOST optimization of signal timings. The first objective was to compare estimates of the fuel consumption from CMEM LDV and HDD models with those computed by VISSIM (node evaluation) based on a formula widely used by major traffic signal optimization tools (TRANSYT-7F and SYNCHRO) (8). The formula used by VISSIM, TRANSYT-7F, and SYNCHRO reads as follows:

where k1=0.075283 −0.0015892 ⴱSpeed +0.000015066 ⴱ speed2 , k2=0.7329, k3=0.0000061411 ⴱspeed 2 , F=fuel consumed (gal), speed=cruise speed (mph),

total travel=vehicle miles traveled (veh mi), total delay=total signal delay (h), and

stops=total stops (veh/h).

The second objective was to show that in order to minimize fuel consumption or vehicular emissions, fuel consumption or particu-F=total travelⴱk1+total delayⴱk2+stopsⴱk3

lar vehicular emissions should be used as an objective function when signal timings are optimized. In other words, when delays or stops are used in the objective function, minimal delays or stops are obtained but not necessarily the minimal fuel consumption or lowest vehicular emissions. Because of the time-consuming opti-mization process, these optiopti-mization experiments were limited to minimize fuel consumption and CO2emissions. CO2does not rep-resent a criterion pollutant, but because of the threat of global warm-ing, controlling this gas has become more important than ever. Other vehicular emissions (CO, HC, NOx) can also be optimized by the proposed VISSIM–CMEM–VISGAOST approach.

Seven optimization experiments were conducted. In total more than 100 control variables for all intersections in the Park City network were optimized to reduce total delay (for the entire network, in hours per hour), stops, throughput (total number of vehicles that completed their trips in the network), PI (PI =total delay +10 * stops/h), CMEM fuel consumption, VISSIM fuel consumption, and CMEM CO2 emis-sions. Multiple optimizations, with various objective functions, were performed to show the difference in the lowest fuel consumption and CO2achieved by each optimization method.

Each optimization started with the same initial signal timings from the field. Each optimization was based on evaluations of traffic and emissions performances accumulated during 60 min of simulation time with an additional 10 min for warm-up. Simulation warm-up is necessary to achieve steady-state traffic conditions in the network. Each optimization had 12,000 evaluations of various signal timing plans; 20 signal timing plans were operated through GA procedures for each of 600 generations. Previous experiments showed that this combination of GA population and generations yields the best results (9). In addition, each signal timing plan was evaluated for five ran-domly seeded simulation runs to account for variability of traffic flows. The optimizations were performed on 20 dual-processor computers. Overall, it took around 20 days of continuous simulation run time to complete the optimizations.


Optimization of CMEM-estimated fuel consumption is shown in Figure 3, which demonstrates how the best fuel consumption and average fuel consumption vary over 600 generations. Spikes observ-able in Figure 3 reflect use of partial optimizations of signal timings (9). Similar trends were observed for six other optimization runs. Most of the final signal timings, which reduce fuel consumption and CO2 emissions, seem to favor major-street operations and yield more delay to the side-street traffic. These signal timings exhibit higher cycle lengths and better progression on major streets. Once the optimiza-tions were finished, each of the seven best signal timing plans was evaluated through 40 randomly seeded VISSIM simulations. VISSIM performance measures were recorded and average statistics were computed. The 40 VISSIM runs were also linked with CMEM. CMEM’s estimates of fuel consumption and CO2were recorded and averaged. Mean values from these statistics are presented in Table 2 for all seven objective functions.

Discussion of Results

Almost all of the optimization experiments found signal timings that reduce CMEM fuel consumption when compared with the initial


signal timings. However, not all of the objective functions worked effectively. Although delay, throughput, and PI could be used as objec-tive functions without additional constraints, the other performance measures were not as effective in this role.

The major problem is represented by the way that an objective function reports performance of the system if there is excessive delay in the network. In such a situation delay and PI significantly increase, whereas throughput significantly decreases. So if the GA procedure suggests a signal timing plan that causes a traffic jam, these three per-formance measures, when used as objective functions in the GA, will detect the problem and such a signal timing plan will be discarded.

However, when other performance measures are used as objective functions, they may not necessarily recognize poor traffic conditions. For example, if traffic is jammed, vehicles move less and hence they stop less frequently (as recorded by VISSIM). So if the stops are minimized in the optimization, the traffic jam will be perceived as a favorable outcome. A similar situation occurs with emissions-related measures. If a vehicle is stopped and idling, it consumes less fuel than

one that runs at 40 mph. So although its fuel-per-mile consumption is higher when the vehicle is idling, its fuel-per-second consumption and emissions are lower. For this reason sometimes both VISSIM’s internal fuel calculation procedure and CMEM report low fuel con-sumption and emissions associated with poor traffic conditions (which are detected by the other performance measures). To illustrate the problem, such an example is provided in Table 2, where VISSIM-reported fuel consumption is used as an objective function (note the minimal VISSIM-reported fuel consumption and the huge increases in number of stops and delay).

Similar problems were observed when three other performance measures were used as objective functions in these experiments (stops, CMEM fuel use, and CMEM CO2). The ultimate solution to this prob-lem might be either selection of the reliable objective function or use of various metrics (e.g., stops and delay) as constraints in the optimiza-tion rather than in the objective funcoptimiza-tion. To test this integraoptimiza-tion, the second method was used. The GA optimizations were constrained in such a way that poor solutions, which minimize one objective function

650 655 660 665 670 675 680 685 0 50 100 150 200 250 300 350 400 450 500 550 600 Number of Generations

Fuel Consumption [gal]

Average Fuel Consumption Initial Fuel Consumption Best Fuel Consumption

FIGURE 3 Optimization of CMEM fuel consumption.

TABLE 2 Measures of Effectiveness from 40-Run Tests Mean Values


MOE Optimized Fuel Use [gal] Fuel Use [gal] [kg] Delay [h] Stops [veh] PI Initial (no optimization) 685.1 780.7 10,349.6 185.1 24,317.1 8,020.1 252.7 Delay [h] 667.2 784.9 10,410.3 163.0 22,501.1 8,087.7 225.5 Stopsa 660.8 786.4 10,251.1 164.1 20,046.3 8,093.7 219.8

Throughput [veh] 674.2 792.8 10,514.4 177.3 26,897.4 8,094.3 252.0

PI 667.2 786.5 10,399.0 164.2 22,895.0 8,115.0 227.8

CMEM fuel use [gal]a 658.8 789.5 10,221.4 170.0 20,679.6 8,120.3 227.4

VISSIM fuel use [gal] 2,011.4 629.9 13,055.2 1,046.0 158,819.7 5,813.9 1,487.2 CMEM CO2[kg]a 661.6 787.4 10,206.8 168.3 21,252.4 8,103.8 227.4 aConstrained optimization results.


but negatively affect all others, are discarded. Table 2 shows the results of such an approach for the three objective functions whose optimizations were constrained (denoted by an asterisk).

The results from Table 2 show that almost every optimization reduces CMEM-reported fuel consumption and CO2emissions. How-ever, optimizations in which these two performance measures were minimized generated minimal results. At the same time, VISSIM’s fuel consumption does not seem to be consistent even when proper performance measures are used as objective functions.

Overall, VISSIM fuel consumption does not represent an accu-rate value when compared with CMEM fuel consumption. The fuel consumption reported by VISSIM should be lower than that from CMEM because VISSIM reports fuel consumption only on the links within node boundaries (user-defined areas around the intersections), whereas CMEM reports total fuel consumed on all links in the net-work. However, results from Table 2 show the opposite trend, because VISSIM does not calculate fuel consumption properly. There are multiple reasons for this inaccuracy in VISSIM’s fuel consumption calculations: the formula used by VISSIM is based on aggregated measures (speed, stops, delay, etc.) and cannot provide a level of accuracy achieved by CMEM calculations; VISSIM does not report fuel consumption for those vehicles that are still within the node boundaries; VISSIM’s formula might be based on outdated emissions characteristics of an average vehicular fleet. Further research is needed to investigate the inaccuracy of the fuel consumption reported by VISSIM.

When CMEM fuel consumption is used as an objective function to optimize signal timings, the optimal signal timings generate sig-nificantly lower fuel consumption than if delay or PI is used as an objective function. The savings in fuel consumption when compared with the delay or the PI are around 1.5%. Although such savings may not be seen as important, it is interesting to see that after 12,000 eval-uations the results do not show that there is a significant difference in fuel consumption between signal timings optimized for minimal delay and minimal PI. In the past, the difference in fuel consumption between signal timings that optimize delay and PI was estimated to be between 1% and 3% (2). The current findings confirm those from Smith et al., who directly minimized fuel consumption (within SCOOT) and reported similar benefits over PI optimization of around 2% (22).


The goal of this study was to present a new integration of existing traffic operation, emissions estimation, and signal optimization mod-els. The study describes the integration of VISSIM, CMEM, and VISGAOST to optimize signal timings in such a way as to achieve minimal fuel consumption and vehicular emissions. The following conclusions were reached:

1. Number of stops, fuel consumption, and CO2emissions do not seem to be reliable objective functions in the optimization of signal timings. Instead, they should be combined with other traffic perfor-mance measures, or additional constraints need to be introduced in the optimization process. Further research is needed to investigate what the best objective function is to minimize fuel consumption and emissions.

2. The VISSIM formula for fuel consumption is heavily influ-enced by number of stops and does not seem to be a reliable objec-tive function to minimize fuel consumption or emissions. VISSIM’s

method of estimating fuel consumption seems to significantly over-estimate total fuel consumption when it is compared with CMEM fuel consumption.

3. If fuel consumption is used as an objective function in a con-strained optimization of signal timings, the optimal signal timings will generate fuel consumption 1% to 1.5% lower than that obtained through a minimization of delay or PI. Although these results may seem insignificant, they have the same order of magnitude as the results obtained from the experiments that first investigated fuel minimization through signal timings (2).

4. Lengthy computation times make application of this research impractical for everyday optimization of signal timings. For this rea-son it is necessary to investigate which combination of delay, stops, and other potential performance measures would lead to minimal fuel consumption.

5. Future research should address additional optimization exper-iments with a variety of traffic networks and scenarios to develop a general strategy of how optimization of certain traffic metrics affects fuel consumption and vehicular emissions. Eventually, results from the simulation testing should be validated by field measurements.


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Sig-nals to Reduce Fuel Consumption. TRL Report LR934. Transport Research Laboratory, Crowthorne, Berkshire, United Kingdom, 1980. 3. VISSIM 4.30 User Manual.Planung Transport Verkehr AG, Karlsruhe,

Germany, 2008.

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Figure 1 shows the integration of VISSIM, CMEM, and VISGAOST to find signal timings that reduce fuel consumption and vehicular emissions
FIGURE 2 VISSIM model of road network in Park City, Utah.
TABLE 2 Measures of Effectiveness from 40-Run Tests


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