A new simulation model of fuel consumption and pollutant emissions in … · 2014. 5. 13. · Pollutant emissions and fuel consumption are influenced by many variables, mainly by

A new simulation model of fuel consumptionand pollutant emissions in urban traffic

D.C. Festa & G. MazzullaDipartimento di piani)cazione territorialUniversity dells Calabria, Italy

Abstract

The atmospheric pollution is one of the most important impacts of vehiculartratlc; air pollution levels and the over-saturated traffic conditions constitutesevere problems in many cities. A large number of models, with differentcharacteristics, has been proposed in order to simulate tratlic flow and relatedpollutant emissions. In this paper, a fuel consumption and pollutant emissionmodel is presente~ which has three interesting features. It is based on averagetratlic speed, and so it can be coupled with macro traf6c simulators its range ofvalidity (1 - 30 km/h) is well suited for urban congested traflic; it reproducesconsumption and emissions in presenee of accelerations, decelerations, stop andgo phenomena, typical of urban traftic. The model has been developed in threesteps. Many experimental journeys have been effected by a test car in a large city(Naples); space and speed have been measured by every interval of 0.5 second.Fuel consumption and pollutant emissions have been computed second bysecond for each journey, using an instantaneous emission model, MODEM;afterwards, total fuel consumption and pollutant emissions in each journey havebeen computed. Statistical relationships between consumptionlemissions rates(g/km) and averagespeed (km/h), observedin the test runs, havebeen derivedbythe least squares method.

1 Introduction

A decision support system, used to evaluate fhel consumption and pollutantemissions in road networks. is compaed of two fundamental modules: the firstone is used to simulate the interactions between land use and transport system,and the characteristics of the produced traffic flows; the second one is used for

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calculating energy consumption and pollutant emission levels, which arefunctions of the relevant characteristics of the traffic flows. The first moduleallows the computation of the flows in the transport network, and relatedfeatures, which are originated by the characteristics of the transport system andof the activity system.A transport model, in the most general case, is used to analyse the effects onmobility, in the middle or long term, of strategical measures (eg construction ofnew transport facilities), or relevant modifications in the land use pattern. Atransport model usually contains four main submodels. The trip generationsubmodel allows the computation of trips originated in each traffic zone I, ineach time interval, for various purposes. The trip distribution submodelcomputes the probability that a trip, originated in the zone I, is directed towardthe zone J. The mode choice submodel computes the probability of using aspecific transport mode (e.g. car, bus, et c,), when moving ffom zone I to zone J.The network assignment submodei computes paths used to reach each zone Jfrom each zone I, for every transport mode. Transport models constitute a wellknown tool; they operate from the urban to the regional or national scale; variousmodels, with similar characteristics, have been developed by researchers; amongall, the EMME/2 model, developed by the CRT of Montreal [1], the TRIPSmodel, developed in the UK by MVA Systematic [2], and the ItalianMT. Model, developed by CSST and ELASYS [3], are instanced.When the transport demand is assumed to be fixed, the transport model reducesto a simple traffic simulation model. These models are used to analyse the effectson traffic characteristics of tactical measure, as new traffic regulations, or localroad network improvements. Many traffic models have been proposed in order tosimulate the traffic flows on a road network; they are usually classified in threecategories.Macroscopic traffic models use a continuous flow representation (fluidapproximation); user journeys on links are not explicitly traced; aggregatemeasures (average speed, average waiting times at intersections) are computed.These models do not capture the speed profiles of individual vehicles or groups;average travel times are estimated for each link of the road network.Macroscopic models are used to simulate large networks The META model,proposed by Papageorgiou et al. [4], for instance, simulates speed and density onthe links; the volume Q is a deduced variable.Microscopic traffic models simulate the movements on the network of eachindividual vehicle, using car following, lane changing, and traffic signal response}Ogic.Microscopic traffic simulators exhibit high levels of complexity, whichconstitute a se~’erelimit to their applicability to large networks; they constitutehowever an efficient tool to evaluate the performances of local networks. Themodel Integration, proposed by Van Aerde [5. 6], assumes a basic relationshipbetween spatial headway and speed for each link; Integration includes analgorithm, which manages vehicle interactions through the basic relationship; arule based lane-changing model, that allows to represent the lane changingmanoeuvres on multi-lane links; and a gap acceptance model, which describesvehicular interactions at onioff ramps.


1_rbcznTransport in the 21st Ccntun 583

Mesoscopic models are an intermediate step between macroscopic andmicroscopic ones. Mesoscopic models deal with platoons of vehicles, which aretraced in their movement between the origin and the destination node. The modelSTODYN-MICE, proposed by Cascetta and Cantarella [7,8], for example,computes the running speed on links using usual speed/density relationships, andwaiting times at intersections using the deterministic queuing theory.Pollutant emissions and fuel consumption are influenced by many variables,mainly by vehicle technology, engine thermal conditions, and motioncharacteristics (speeds and accelerations). Emission models are used to estimateemission factors, namely pollutant emissions referred to time and distance units,or to specific driving sequences. Emission factors are related to homogeneousvehicles groups; composite emission factors reflect the actual flow compositionon a specific link. Thermal conditions are specified by cold start, transient andhot-stabilized emission factors. Fuel consumption and pollutant emissionmodels, too, have specific characteristics, depending on the model aim and thereference scale. At regional and national scale, these models are used to estimatethe global energy consumption and to compute pollutant emission inventories;these parameters are used, for instance, to evaluate the effects, in the long period,of transport policies, strategical planning measures, nation-wide infrastructuralplans, or more stringent regulations for vehicular emissions and so on, on theoverall air quality. At urban scale, emission models are used to evaluate theeffects of traffic measures, or local new transport infrastructures, on localemissions levels; the effects on local air quality may be also estimated, usingpollutant dispersion model, as CALINE [9], or ITALICS, 1 [10].Emission models are usually classifies in two categories. Average modelscompute fuel consumption and pollutant emissions, which occur in a trip, by theaverage speed in the whole trip; these models are essentially used for large scaleinventories purposes. The model MOBILE is used in the US since the late 1970s[1 1], and has been upgraded several times; the model CORJNAIR has beendeveloped for the European car park [12]; an updated version, COPERTII, hasbeen recently released [13], This model takes in consideration the various abovementioned factors, which affect emission rates. Instantaneous, or modal models,compute consumption and emissions, which occur in a trip, summing the valuescomputed for each elementary time interval (usually 1 second) by theinstantaneous speeds and accelerations, These models are used for local studies;they allow to compute the distribution of consumption and emissions along aroad section. The model MODEM [14] calculates the emissions of fourpollutants (CO, HC, NOX, COZ), and the fuel consumption, second by second,according to the speed curve and taking into account the acceleration, for 12 carlayers (4 types of cars: gasoline cars without catalyst, complying with 15/03 or15/04 European standard, gasoline cars with controlled catalyst, Diesel cars; and3 classes of engine displacement: 2.0 1). Instant emission andconsumption values are summed to obtain the total values in the whole drivingsequence.Emission models must be consistent with the models used to simulate trafficflows, Average emission models can be coupled with macroscopic or


584 ( ‘h)l Trcmsport in the 21st cenmI?

mesoscopic traffic simulators, which reproduce the average link speeds; modalmodels can be coupled with microscopic traffic simulators, which reproduceinstantaneous speed and acceleration for each individual vehicle in the network,Average emission models, however, have been usually developed forregionah’nationa] inventories purposes, and they are not fit to reproduce the localcongested traffic, which occurs in many urban areas; slow speed in urban trafficmay be outside the range of the models (e.g. 10 – 100 km/h). These models,when used for urban traffic, usually underestimate consumption and emissionrates, Instantaneous emission models, instead, are well suited to congested trafficconditions; however, they must be coupled with microscopic traffic simulators,which require an high computational effort,Because of these reasons, much research work has been devoted to developsimpler tools in order to estimate pollutant emissions in function of macroscopictraffic variables. Proneilo and Andre [15] have implemented a modified versionof MODEM, named MODEM2; this version requires the average speed andacceleration in each driving sequence, instead of the instantaneous values secondby second. In this paper, a new model is presented, which has, as input values,only the average speed in the whole trip, or along an individual road section. Thenew model, so, can be coupled with macro and meso traffic simulators, whichreproduce only aggregate traffic variables, as the average speed. However, themodel has been calibrated for the average speed, which occurs in real urbandriving cycles encompassing positive and negative accelerations, stop and gophenomena, and constant speed conditions too. So, the new model “statistically”reproduces the effects of instantaneous traffic variables (accelerations andspeeds); the model thus overcomes the underestimation problems, which verifiwhen average models are used in order to simulate fuel consumption andpollutant emissions in congested urban traffic.

2 Construction of driving cycles

The journey characteristics (speeds, accelerations, waiting times) used in thisresearch work, for the construction of driving cycles, were collected by a test car;the car was a two-liter engine passenger vehicle, which can be powered bygasoline or methane (FIAT Marea Bi-Power). The car was equipped with aGlobal Positioning System (GPS) device and a data acquisition system.Monitored parameters include geographical coordinates (latitude and longitude),instantaneous speed, covered distance, and some engine parameters, Theparameters were monitored at 500 milliseconds intervals; for each parameter,23.829 values were recorded.The car was used in real traffic conditions in Naples suburbs; the driver followedthe main traffic stream, Test runs were performed on July 30, 1999, from 8.20a.m. until twelve. The collected data were used for the construction of drivingcycles. The test path included several urban streets, with one signalisedintersection; several experiment journeys were effected; the total travel distancewas about 75 km, The recorded data include two trips (about 22 km) on an urbanmotorway, from the car garage to the test site. Traffic flows were quite high; the


L-rban Transport in the 21st Century 585

test car was forced to stop and go, according to local congestion phenomena,traffic lights at the signalised intersection, and traffic rules at the unsignalisedones. Using simultaneously instant GPS and travel distance data, the car positionon the test path was traced second by second. The total travel space was dividedin several driving sequences; in each one, the car starts from stop conditions,runs for some time, and then again stops (fig. 1). The number of identifiedsequences was 99; for each one, the following parameters were computed:

● Length (metres)● Running time (seconds). Stop time (seconds)● Total travel time (running time plus stop time, seconds). Running speed (length/running time, metres/second)● Travel speed (length/travel time, metres/second)● Standard deviations of running and travel speeds (metres/second). Average running acceleration and average travel acceleration (m/sec2)● Average positive acceleration in travel and running times (m/sec~)● Average negative acceleration in travel and running times (m/sec2).

(w/(O 15 30 45 60 75 93 105 120 135 150 165

Tum (EQ

RunningTtme StopTime

~4+

TravelTime

Figure 1: A typical driving sequence.

The driving sequences are classified according to the travel distance and therunninghravel speeds. The average cycle length is 371.4 metres; the 90thpercentile, which excludes the longer trips on the urban motorway, is 1976.9 m.,the 75tt’percentile is 754.7 m, the 25ti’is 35.65 m, and the 10t)’percentile is 3.3 m.The average travel speed is 4.94 m/s (17.78 km/h), whiie the average runningspeed is 5.69 rn/sec (20.48 km/h). The cumulate distributions of the two speedsare com ared in figure 2.

tThe 90 percentiles are respectively 6.75 and 7.19 rdsec; the 75[h percentiles6.13 and 6.54; the 25ti percentiles 0.99 and 2.01; the 10ti percentiles 0.20 and0.59; the two distributions exhibit higher differences in the lower range of


speeds; it has been observed that differences among travel and running speedsare higher in short than in long trips. The average accelerations, in a drivingsequence, range to 0.02 rn/sec2 in travel conditions, and 0.07 m/sec2 in runningconditions; obviously, the average value, in each driving sequence, tends to zero.The average positive acceleration ranges ffom 0.28 to 0.92 m/secz, while theaverage negative accelerations range to 1.56 m/secz.

, I

,——. .. . . -.—- —— ——.

Figure 2: Cumulate distributions of travel and run speed in driving sequences.

3 Fuel consumption and pollutant emissions in the observeddriving cycles

Fuel consumption and pollutant emissions, in each of the observed drivingcycles, where computed by the software MODEM, using instantaneous speedsand accelerations. as measured in the test runs; obviously, speed and accelerationwere zero, when the vehicle was waiting in a queue, or otherwise idling.Computations were effected for four categories of cars (ECE 15/03 vehicles,ECE 15/04 vehicles, three-way catalyst vehicles, and diesel vehicles); for eachcategory, three classes of engine displacements were considered (less than 1.41,1.4-2,01, greater than 2.0 1).For each type of vehicle, the emissions of the fourmain pollutants, CO, HC, NOX, COZ,and the fuel consumption, were computedfor each second of each driving sequence; total values were then computed foreach driving sequence. Total consumption and emissions were divided by thesequence length and duration (travel time and running time), obtaining theaverage consumption and emissions factors for the unit of space and time (g/kmand g/see) in each driving sequence. The values of the estimated parameters forthe test car (gasoline catalyst vehicle, 2.0 Iitres displacement), are shown in table1. The range of each computed value is very large; this indicates that trafficconditions affect heavily energy consumption and pollutant emissions.Differences between the same parameter in running and travel conditions arehigh; this indicates how the repeated stops in urban traffic constitute a seriousproblem non only because of the lost time value, but also because ofenvironmental impacts.


L}barr Transport in the Ilst Centwy 587

4 Regressive models for fuel consumption and pollutantemissions

Thecomputed data were used toproduce an interpretative mathematical modelwhich, based on statistical relations, could connect the consumption andemission rates with the average running or travel speed. A preliminary statisticalanalysis was performed in order to investigate the degree of correlation amongthe variables involved in the phenomenon:

●

●

●

●

●

●

●

●

●

●

Pollutants emission rate in running and travel conditions (g/km)Fuel consumption rate in running and travel conditions (g/km)Length of the driving sequence (m)Average running and travel speed (m/see)Standard deviations of running and travel speed (m/see)Average positive and negative accelerations in running conditions(rn/sec2)Average positive and negative accelerations in travel conditions(m/sec2)Total travel time (see)Total running time (see)Total waiting time (see)

Table 1. Average emission and fuel consumption rates.

Parameter Type of motion Minimum Medium Maximum

CO (glcm)Run 255 15.67 155,92

Travel o 28.58 459,18

HC (g/km)Run 0032 151 48.00

Travel 0037 1.63 4911

NO, (g/km)Run 0.27 0.96 1212

Travel 0.27 1,81 2747

co, (gkm) Run 149.93 902.21 8,853.33Travel 149,96 1,962.59 34,919.58

FC (g)km)Run 48.58 288.75 2,863.33

Travel 48,59 63009 11,25390

The correlation among CO emission rates, for the test car, and the cinematicparameters, is reported in table 2. The table shows that CO emission rates areaffected by each cinematic parameter, and mainly by speed, speed standarddeviation, positive and negative accelerations. High correlation degrees do existamong many cinematic parameters, and collinearity problems arise, if aregressive model among CO emission rates and all cinematic parameters isestimated.Various functional forms were so investigated, in order to produce aninterpretative mathematical model, which could connect consumption andemission rates with the various cinematic parameters; the analysis showed that


the two rates were mainly affected by the running or the travel speed. Twomodels were so calibrates, having the following equations:w ~=a*~

● y=i + c*v+ d* V2where y is the pollutant emission rate or the fuel consumption rate, and V is therunning or the travel speed.

Table 2. Correlation matrix of parameters (r: run, t: travel; w: wait).

Q ~

~ ~ : ~ < ~ “$g g ~

~& ~ g g ~ g ~ ~ Q ~ ~

o 6 e = ~2 u 2 >“ 5 m m ‘~ ‘G ‘: ‘~ +- +’ :-

COr 1.00

co, 0.85 1001

L -022 -019 100 I

Vr -055 -049 071 I 00

v, -0,47 -043 07’4 095 1.00

SD(V,) -057 -0.51 0.58 094 0.87 Loo

SD(V,) -0.58 -053 055 094 0.87 099 1.00

3’, -058 -053 003 0.48 037 059 0.58 i .00

a“, 0.51 048 -0,06 -050 -03s -059 -0.59 -0.73 1.00

a’l -057 -053 003 0.48 037 059 0.58 Loo -073 1.00

a-l 0.53 049 -012 -055 -0.46 -0.61 -0.61 -071 091 -071 1.00

T, -0.29 -0.24 088 0.65 066 052 0.51 0.14 -0.14 014 -023 1.00

T, -0.29 -0.26 0.89 0.67 071 0.54 0.53 013 -0.14 013 -0.23 0.99 1.00

T,, -005 009 -013 -017 -0.34 -012 -013 0.04 -004 004 002 0.01 -012 1.00

The best results were obtained by the first model, which exhibit a betterstatistical fit. CO emission rates, for the four categories of vehicles, are reportedin table 3; similar relationships have been obtained for HC, NOX and COZemission rates. Fuel consumption rates have been reported in table 4. Figure 3shows the regression curves between CO emission rates and running/travelspeeds.When using the model referred to ruining speed, fuel consumption and pollutantemissions in a driving sequence must be computed in three steps. In the firststep, consumption/emissions in running conditions are computed, multiplyingthe driving distance (km) by the emission rates (g/km) referred to the runningspeed (mlsec). In the second step, consumption and emissions in waitingconditions are computed, multiplying the emission rates, referred to time (g/see),by the time spent in waiting (see). In the third step, the two values are added, toobtain the total consumptiotdemissions in the whole driving sequence. This


L’rban Transport in the 21st Centary .589

procedure is well suited for traffic simulation models, which compute separatelyfor each link the running and the waiting time.When the model referred to travel speed is used, fuel consumption and pollutantemissions in a driving sequence are computed in only one step, multiplying thedriving distance (km) by the emission rates (g/km) referred to travel speeds;these rates encompass consumption/emissions in running and waiting conditions.

.: W .;

: d IoQ

~:” . ...Ij>46 U1O1~l~ 161820

Fuming$xszl(nkc)024G8 I(112 )4 16 182(1

Travd@d(m/itc)

Figure 3: Regression curves between CO emission rates and run/travel speeds

Table 3. CO emission rates (g/km),

Vehicles Running conditions Travel conditions~ateoorie~ Displacement

b Emlsslon rate (#km) R’ Emission rate (g,km) R2

< 1.41 160.170”V” ‘]27” 09239 143,] lo$v~tx?l 09304

ECE 15-03 14-2,0 I I03,03*V41M177 09017 272,88 *VI 2>2,0 I 379,4* V-I ~~~ 09523 341,71*V4)’)W 0.9475

< 1.41 l(33,55*v4j8(N~ 0.8933 101,7I* V”’’8’92 0.9166

ECE 15-04 1.4-2.01 146.96*V’’%UY 0.9254 i40,6g*v.(]~{}z~ 0.9360

>2.0 I 98,244*VO ams 0,9006 102,O1*V48776 0.9261

2.0 I 2,,553 *V417017 0.7962 20,6030*V4] 7[]14 0.8638

2,01 12,8*V

590 [’rban Transport in the 21s[ Cemoy

computed for some values of speed by the model referred to running speed, themodel referred to travel speed, and the model CORINAIR, which is referred toaverage speed.

Table 4. Fuel consumption rates.

Vehtcles Running conditionsDisplacement

Travel conditions

categories Fuel cons. rate (g/km) R’ Fuel cons. rate (g/km) R’

2.0 I 576.34*V4J 8$’)] 09014 539.57* V”’ ’45” 0.9205

Llban Transport in the 21st Centav 591

running speed, are quite greater than values computed by the rrmdel referred tothe travel speed. This is not surprising. In each driving sequence, the averagerunning speed is greater than the average travel speed; if consumption/emissionrates are computed for the same value of speed, the values in running conditionsmust be greater than the values in travel conditions. The difference between ratesin running and travel conditions decreases when speed increases, since(statistically) the influence of time lost in waiting conditions decreases.Emission and consumption rates, computed by the proposed models in the range0.25-10 rmkec, are greater than values computed by the model CORINAIR; forhigher speeds, the three models tend to predict the same rates. This is obvious,since CORINAIR has been developed for large scale inventories, and is referredto an average travel speed; the new models, instead, have been calibrated inorder to reproduce emissions and consumption rates in congested urban traffic.

6 Conclusions

Pollutant emission and fuel consumption models, which have been developed tomake out inventories at regional or national scale, refer to the average travelspeed; they are not fit to congested traffic conditions, typical of many urbanareas. Modal emission and consumption models refer to instantaneous speed andacceleration; they capture urban traffic dynamics, when are coupled tomicroscopic traffic simulators. These system of models are used to evaluate localtraffic measures, while their use for large networks is difficult, because of thehigh computational effort. Simpler tools, related to macroscopic traftlc variables,are so requested in order to simulate emissions and consumption in urbannetworks, for planning purposes. The analytical models, presented in this paper,refer only to the average speed, and so they can be coupled with usual trafficmacro and meso simulators; however, their results are comparable with resultsproduced by the more sophisticated instantaneous models.

Acknowledgements

The authors would like to thank Elasis S.C.p.A., which has provided the database on the test driving sequences, and engineer Giuseppe Monteleone, for hiscooperation in the data analysis.

References

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592 [‘vbnnTmnspwt 1??the 21st Centq

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A new simulation model of fuel consumption and pollutant emissions in … · 2014. 5. 13. · Pollutant emissions and fuel consumption are influenced by many variables, mainly by