Center for Energy and Environmental Studies IVEM 9747 AG ... · Average car ownership level...

A comparison of short distance transport

modes

M.E. BouwmanCenter for Energy and Environmental Studies IVEMUniversity ofGroningenNijenborgh 49747 AG GroningenThe Netherlands

Email: [email protected]

Abstract

This paper presents a comparison of seven transport modes in both urban andrural settings, based on four characteristics of transport modes: space use, energyuse, costs and travel time. The characteristics are calculated with a computermodel and based on these results the modes can be ranked. This paper shows -based on preliminary results - that the ranking order of the various transportmodes based on the score on the four variables is not very sensitive to the spatialsetting, although differences between spatial settings exist for the characteristicsof the modes. By extending the analysis to the year 2020, slight differences occurin the ranking order of the transport modes, but no differences are found betweenthe spatial settings.

1 Introduction

Within environmental sciences, energy use and related emissions are importantresearch topics. For transportation, combustion of fossil fuel results in thegenerating of e.g. CO], H?O, NO%, SOx, etc. The emission of these substancesmay give rise to environmental problems. Transportation has a big share inseveral of these emissions (Jepma [1]). In 1995, it emitted 61% of all CO in theNetherlands, 62% of the NO.,, 40% of particles and 22% of all SO:, and it had ashare of 18% in energy use and CO: emissions (RIVM [2]). As transportaccounts for a major share in the overall energy use and emissions, transport is ahighly interesting subject for environmental research. In this paper, energy use oftransport is used as an indicator of the environmental impact of transport causedby energy use and emissions.

Transactions on the Built Environment vol 41, © 1999 WIT Press, www.witpress.com, ISSN 1743-3509

416 Urban Transport and the Environment for the 21st Century

There is a wide variety of options for energy conservation in transport. Oneof the energy saving options is to switch to modes that have a relatively lowenergy consumption. This paper focuses on the assessment of transport modeswith a minimised environmental impact per travelled kilometre. The firstquestion arising is how to determine what mode has the smallest environmentalimpact, or in other words, how to rank the available transport modes according toan increasing environmental impact per travelled kilometre. Besides, it isinteresting to determine whether such a ranking order changes under varyingcircumstances.

This paper discusses whether the transport mode with the smallestenvironmental impact can be identified, and the sensitivity of the environmentalranking order for specific variables, such as the spatial setting. Section 2discusses which characteristics of transport modes are relevant in determiningthe environmental ranking of the modes. Section 3 shows in more detail thedifferences between the various spatial settings, and why this subdivision isworth looking at in more detail. Section 4 discusses the method used and section5 presents some results.

2 Transport modes

For inner-city transport, a variety of transport modes is available. In general, thevariety of transport modes is bigger in larger cities than in smaller ones, as publictransport systems are more profitable in bigger cities. From an environmentalpoint of view, it can be interesting to investigate whether energy savings arepossible by changes in the modal split.

From a variety of recent research reports (e.g. Steg [12]), one may concludethat changing the current modal split is not easy. So, before trying to influencethe modal split, it is important to see which modes are environmentallypreferable, and whether these modes are favourable in all situations. Next to that,it is important to see how these favourable modes score on other elements thatinfluence travel mode choice, such as speed and costs.

To discern environmentally friendly modes, two variables are introduced.The energy use of a transport mode represents much of the total environmentalimpact, as most of the harmful emissions are directly associated with energy use.Next to that the space use is used in the analysis. Space is also a scarce good,especially in cities. Moreover, space has a high value in densely populated areas.The space use in this analysis is used as an indicator of some of the otherenvironmental problems associated with transport, such as noise and stench.

Speed is an important transport mode characteristic. It appears thatindividuals have a relatively fixed time budget that they can spend on transport(e.g. Schafer [13], Hupkes [14], and Zahavi [15]). This budget comprises aboutone hour to one hour and a half a day. By increasing the travel speed, theindividual mobility can be expanded without exceeding the travel time budget.

For costs, a similar reasoning is valid. Households spend on average about 15percent of their budget on transportation (CBS [9]). Especially for individualswith low incomes, cheaper modes might imply an expansion of theiropportunities.


Urban Transport and the Environment for the 21st Century 417

In mode choice, other variables play a role too, such as reliability, comfortand habits. These characteristics are less easy to quantify than costs and speed ofmodes. Therefore, costs and speed are used as indicators of the personalpreferences, based on the idea that cheaper and faster modes can contribute to ahigher personal mobility, which seems to be an overall goal of many individuals.

Based on these considerations, an analysis will be performed on thepossibility of ranking various modes, based on four variables. Two variables,space use and energy use, represent the environmental score of the modes. On asocietal level, it is preferable to minimise these issues. Two other variables, costsand speed of modes, represent important characteristics of modes for anindividual. In the analysis, all modes will have different scores on each of thesefour variables. By comparing these scores, modes can be distinguished.

3 Spatial settings

The described important variables of transport vary largely among various spatialsettings. Not only do patterns of mobility vary with various spatial settings; thecharacteristics of transport modes may also change in different circumstances.

Large cities are often associated with environmentally attractive mobilitypatterns. This results from the fact that in a city -compared to a rural structure-more activities are possible in a smaller area, implying shorter travel distances.Greater access to public transport also makes it a better alternative compared totravelling by passenger car. This expectation is largely confirmed when lookingat mobility patterns in existing situations with high and low population densities.For example, table 1 shows the modal split (km/day/person) in a very stronglyurbanised (more than 2500 dwellings per square kilometre) and a rural (less than500 dwellings per square kilometre) situation in the Netherlands in 1996.

Table 1. Modal split in

Passenger car - DriverPassenger car - PassengerPublic TransportMopedBicycleWalkOtherTotal

the NetherlVery strongly12.15 km7.79 km7.42 km0.18 km2.92 km1.17km0.56 km32.18 km

ands, 1996urbanised37.8%24.2%23.1%0.6%

9.1%3.6 %1.7%

100 %

(km/day/;

18.20 km9.66 km2.90 km0.28 km

2.66 km0.66 km0.92 km35.29 km

lerson)Rural

51.6 %27.4 %8.2%

0.8%

7.5%1.9 %26%

100 %

Source: (CBS [7])

The total distance in kilometres differs about ten per cent among the twosituations. Table 1 does not list the statistical data on three other situationsbetween the two presented extremes. These intermediate situations show totaldistances of 34.11 km, 33.43 km, and 35.02 km respectively. This indicates thattravel distances indeed vary among spatial settings, although the difference



between very strongly urbanised settings and all other settings is bigger than thedifference between two other settings.

Table 1 shows a clear difference in the modal split of the two situations. Inrural areas about 75 per cent of all kilometres are travelled by passenger carcompared to about 60 per cent in compact cities. The difference of 15 per centseems to be travelled on foot or with public transport in the compact city, whichseems to correspond with the expectations indicated earlier.

The observed mobility differences cannot be attributed fully to thedifferences in population density. Income is also a contributing factor for theobserved differences in the modal split. The use of public transport in a compactcity may be higher due to a greater access to this mode of transport. The share ofpublic transport might also be lower in rural areas, because people who canafford to own a car prefer to live in rural areas. This second argument shouldcorrespond with clear income differences in the various situations, as it is knownthat car ownership levels are strongly correlated with income levels (see forexample Korver [3]; CBS [10]). Table 2 gives information about differentcharacteristics of households in various spatial settings, as well as their access toprivate transport.

Table 2. Household size, income, car ownershipand use in different spatial settings

Urban density

Average household size(person/household)Average standardised income(Dfl * thousand /year)Average car ownership level(cars/household)Share of households withouta car (%)

Average annual kilometrageof a passenger car (km/year)Average daily distancetravelled (km/day/person)

vsu

2.19

30.2

0.69

39.9

15,540

32.18

SUR

2.41

32.0

0.89

25.0

15,640

34.11

URB

2.54

326

1.02

17.5

16,010

34.43

WUR

2.69

33.0

1.06

15.3

17,040

35.02

RUR

2.73

322

1.12

12.9

16,750

35.29

Legend: VSU Very strongly urbanised, SUR Strongly urbanised, URBUrbanised, WUR Weakly urbanised, RUR RuralSource: (CBS [4], CBS [5], CBS [6], CBS [7], CBS [8], and CBS [9])

Table 2 shows that the average household size corresponds systematically todifferent population densities. This forms a clear indication that differenthousehold types live in different spatial settings. So, not only the populationdensity correlates to the differences in mobility patterns, also the type ofhousehold does, as people have different mobility patterns in different phases oftheir life. The use of passenger cars is largely influenced by the access to it. Asmentioned above, income levels strongly correlate with the ownership level ofcars. With decreasing population densities, the average income (corrected for thehousehold size) varies; it increases from very strongly urbanised to weakly



urbanised, and decreases in the rural case. The latter largely corresponds with thedifference in household size; the average income is similar in both situations, butthe larger average household size corresponds with a lower standardised averageincome in the rural areas. Car ownership (number of cars per household, as wellas share of households with a car) increases with decreasing population densities.

All the aspects referred to in table 2 contribute to the observed differences inmobility. It is generally agreed on that changes in income and household explainabout two third of the variation in mobility, and that the last third is explained bydifferences in spatial settings.

4 Model description

A model is developed and used to calculate the characteristics of the variousmodes. Seven transport modes are distinguished: three passenger cars (fuelledwith petrol, diesel and LPG), the train, a public transport combination of bus,tram and metro, the bicycle and walking. Although the full range of trip lengthsis included in the model, this paper will focus on trips shorter than ten kilometre.This trip length represents those trips that mainly take place within the built-uparea.

The model is based on the Dutch situation, and uses 1996 as the base year fordata input. Calculations can be made for the period 2000 - 2020. Calculationsare made for the various spatial settings described in section 3. In this paper,results will be presented for the two most extreme situations, the rural setting andthe very strongly urbanised setting. The latter represents the mobility of theinhabitants of the thirteen biggest cities of the Netherlands.

Characteristics of the transport modes for the year 2000 are based on thehistorical fleet data and the corresponding technical characteristics. Forcalculations in future years, the various fleets are updated annually by replacingthe oldest vehicles with newer ones. The calculated energy use not only dependson the technical characteristics of the vehicle, but also on the average speed.Driving at higher speed results generally in a higher energy consumption.Furthermore, frequent stop and drive situations in inner city driving increases theenergy use. For calculating the energy use of the various modes, not only thedirect energy use is taken into account. Also the indirect energy use associatedwith the production and maintenance of vehicles and infrastructure is included inthe analysis. The total energy use of these two contributing components isascribed to the total number of kilometres driven with or on it.

Figure 1 gives an overview of the structure of the model. The fourcharacteristics of the modes, the output of the model, are situated at the right sideof the figure. Only the most important relations are represented in the figure.Next to the variable prices for tickets and fixed expenditures as car ownershipand annual tickets, the costs of the modes are also based -if relevant- on the fueluse. The fuel or energy use depends on both the driving speeds of the passengercar, and the average vehicle characteristics. These fleet characteristics areinfluenced by the rate of introduction of new vehicles, which depends on theaverage lifetime of the vehicles and the size of the mobility demand.



Mobility

demand

Vehicle

characteristics

Figure 1. Model structure

The space use is defined by the amount of infrastructure and the totalmobility demand. The more the infrastructure is used, the lower the space use pertravelled kilometre.

The last variable, the travel time per kilometre depends on the vehicle speed,and on the amount of available infrastructure. When the infrastructure does notoffer enough capacity, congestion will occur which increases the travel time.

The results are presented per kilometre. However, in comparing the results ofthe various modes, an extra correction is needed. Most individual modes can beused from destination to destination, while for public transportation often adetour is needed. In most big cities, bus transport is organised in a star shape,where most busses come and go to one central point. In this structure, the detourcan become pretty large. The detour factors used are shown in table 3.

Transport mode

Petrol passenger carDiesel passenger carLPG passenger carBus, tram, metroTrainBicycleWalk

Table 3. Detour factorsTrips under 10 km

vsu1.11.11.11.21.31.01.0

RUR1.01.01.01.21.31.01.0

VSU: Very strongly urbanised, RUR: Rural

5 Model results

The results shown below are based on preliminary calculations, and should beinterpreted with some caution. The results are shown per kilometre, and a



subdivision is made to spatial setting. Table 4 shows the results on the fourvariables in two settings in the year 2000.

Table 4. Model results for 2000, the Netherlands, trips <10 kmTransport mode

Petrol passenger carDiesel passenger carLPG passenger carTrainBus, tram, metroBicycleWalk

VeiS0.80.80.80.50.90.80.8

ry stronglyE2.82.72.80.92.10.00.0

urbaniT1.81.81.80.92.04.512.8

sedC0.50.30.30.30.30.10.0

S0.60.60.60.50.80.80.8

E2.42.32.40.92.00.00.0

RuralT1.51.51.50.91.84.312.7

C0.50.30.30.30.30.10.0

Legend: S = space use (10 m'Vkm), E = energy use (MJ/passenger km), Ttravel time (min/km), C = costs (Dfl/km)

Note that even with the detour factors, the results cannot be interpretedunambiguously. Even with the restriction of the distance of trips to tenkilometres, not all modes can be equally used. Walking is only suited for theshorter trips in this category, while the train can only sometimes be used at theupper end of the trip lengths. Bus, tram and metro are not an alternative for veryshort trips. Passenger car and bicycles can be used over the whole distance rangeof trips below ten kilometre. These restrictions on the use of modes should becombined with the fact that public transport systems generally extra transport toarrive at and depart from the stations and bus stops. This should be taken intoaccount in interpreting the results.

The various passenger cars have values that only differ in costs. This can beexplained by the Dutch tax system, which makes diesel and LPG cars onlyinteresting in case of high annual use. Both car types have an annual use of overtwice the annual use of a petrol car. This results in dividing the fixed costs over agreater number of kilometres. Next to that, the fuel concerned is cheaper.

The space use is based on the area used and the amount of kilometrestravelled on the infrastructure. For this reason, space use is relatively low forhighways, on which a large amount of kilometres are travelled. Bus, tram andmetro score relatively bad, because next to the normal share they have in generalroads, also the surface area of specific infrastructure like rail and bus lanes isdistributed to these systems. The soft modes walking and cycling also haverelatively high space use. The total area comprised by sidewalks and bicyclelanes is not very big, but the amount of kilometres travelled on this infrastructureis also relatively small. One may question however, whether the full space use ofsidewalks should be ascribed to the function of walking, as these are also usedfor other functions.

The travel time is based on average calculations. This means that effects ofcongestion are distributed over all trips made. Although some trips will faceconsiderable delay due to a lack of capacity of the infrastructure, the overalleffects can hardly be noticed. The travel time shows large differences betweenthe modes as well as between the spatial settings. Passenger cars faceconsiderably more delay in an urban environment than in villages, generally



resulting in a higher travel time value. The good score for the train is due to thefact that this mode faces hardly any barriers in the actual traffic. For this reason,the travel time scores the best of all modes. In practice, the train can seldom beused as a sole mode, like mentioned above, and should be combined with othermodes, in order arrive at and depart from railway stations. Train use on shortdistances is not very likely. The soft modes walking and cycling differ from theother modes by relatively long travel times. Apparently, muscle force posessome clear restrictions on the maximum achievable speed.

Differences between the spatial settings cannot be found for all variables.Space use is slightly lower in rural areas for passenger cars, by the greater use offaster roads, such as highways. Energy use and accompanying costs forpassenger cars are higher in case of urban transport. Table 5 displays the order ofthe results.

Below, the modes are classified for each of the four variables. In this way, theinformation on the differences in scores on the variables is lost, but theinformation is easier to interpret. The ranking scores on the various variables canbe added to get one overall score of the modes, again, without an indication onthe underlying differences.

Table 5. Ranked results for 2000, the Netherlands, trips < 10 kmTransport mode


S3331756

Very strE7563421

DnglyT3331567

urbanise

7653421

;dTOT2017178201515

S3331756

E7563421

RuralT3331567

C

653421

TOT2017178201515

On the variables costs and energy use, walking has in both cases the bestscore, while on the other variables it scores almost worst of all modes. The trainhas the best score on both the space use and the travel time in both settings, withthe caveat that the train can hardly be used as a sole mode. Although in the actualresults of the various modes differences could be noticed between the spatialsettings, these differences cannot be found back in the order of the results.Apparently the differences among the spatial settings are smaller than thedifferences among the various modes.

On the overall score, train has the best score, followed by the soft modeswalking and cycling. Motorised road vehicles as the passenger car and the bussesscore relatively poor in this ranking procedure.

In order to present results for coming years, assumptions should be made onthe development of the mobility demand, the population size, the technicalcharacteristics of the vehicles and the changes in the available infrastructure. It isassumed that the population will develop according to the mid-scenariopresented by CBS, and that annual mobility will grow by 1.0 percent per year.Technical improvements are based on the database provided by Ybema [11].



This database shows a greater potential for efficiency for passenger cars andbusses than for trains. The overall length of the road infrastructure is assumednot to grow with the exception of the roads within the built-up areas for theopening up of new residential areas. Results of the analysis with theseassumptions are shown in table 6.

Table 6. Model results for 2020, the Netherlands, trips < 10 kmTransport mode


VeiS0.60.60.60.40.70.60.6

•y stronglyE2.11.92.50.91.30.00.0

urbaniT1.81.81.80.92.04.512.8

sedC0.40.30.30.30.30.10.0

S0.50.50.50.40.60.60.6

E1.81.62.10.91.20.00.0

RuralT1.51.51.50.91.84.312.7

C0.40.20.20.30.30.10.0

Legend: S = space use (1(T nr/km), E = energy use (MJ/passenger km), Ttravel time (min/km), C = costs (Dfl/km)

Table 6 shows clear energy efficiency improvements compared to table 4,especially for passenger cars. The 2020 value does not equal the most efficientpassenger car in that time, as still older cars are also present in the fleet. For thisreason, in the short term bigger efficiency improvements can be achieved forroad vehicles than for trains, as trains have a longer lifetime. The space use hasgenerally declined, since no road extensions were made, while the total mobilityincreased.

Table 7. Ranked results for 2020, the Netherlands, trips < 10 kmTransport mode


S3331765

Very stnE6573421

DnglyT3331567

urbanisC7435621

edITOT! 19! 15! 161 10I 22! 161 14

S3331765

E6573421

RuralT3331567

C7435621

1 TOT1 191 15! 16! 101 221 161 14

Table 7 shows the results of table 6 classified to order. It shows clearly theimproved position of the passenger car compared to the other modes, due to therelatively big energy efficiency improvements for this mode.

6 Conclusions

Although differences in the two spatial settings exist for most of the variables,based on preliminary results no differences occur in the overall order of transportmodes. A dynamical analysis can add important information, as the order of thetransport modes can change due to the introduction of new technologies.



The energy use for passenger cars is about 15 percent lower in rural situationsthan in very strongly urbanised situations. The results presented in this paperrank the passenger car relatively low compared to other modes. As the passengercar has a major share in the current modal split (see table 1), this indicates thatsome savings are possible indeed by changing the modal split.

Acknowledgements

The author likes to thank Henk Moll, Rene Benders and Ton Schoot Uiterkampfor their comments on earlier versions of this paper.

References

[1] Jepma, C. J., Asaduzzaman, M., Mintzer, I., Maya, R. S. & Al-Moneef,M., A generic assessment of response options, In: J. P. Bruce, H. Lee &E. F. Haites (Eds.), Climate change 1995. Economic and socialdimensions of climate change, p. 225-262,1995

[2] RIVM, Nationale milieuverkenning 4.1997 - 2020, 1997[3] Korver, W., Klooster, J., & Jansen, G. R. M., Car - Increasing

ownership and decreasing use?, In: I. Salomon, P. Bovy & J.-P. Orfeuil(Eds.), A billion trips a day, p. 75-100,1993

[4] Centraal Bureau voor de Statistiek, De mobiliteit van de Nederlandsebevolking in 1994, 1995

[5] Centraal Bureau voor de Statistiek, Stand en loop van de bevolking pergemeentegroep naar stedelijkheid gemeenten, 1993, In: Maandstatistiekvan de bevolking, 1994 nr. 11, p. 44-45,1994

[6] Centraal Bureau voor de Statistiek, Statistiek van de motorvoertuigen,1995

[7] Centraal Bureau voor de Statistiek, Onderzoek verplaatsingsgedrag1996, 1997

[8] Centraal Bureau voor de Statistiek, Regionale inkomensverdeling 1994.Kerncijfers, 1996

[9] Centraal Bureau voor de Statistiek & Kluwer Voertuigtechniek, Auto'sin Nederland, 1996

[10] Centraal Bureau voor de Statistiek, Zakboek verkeer en vervoer 1995,1995

[11] Ybema, J. R., Lako, P., Gielen, D. J., Oosterheert, R. J. & Kram, T.,Prospects for energy technology in the Netherlands. Volume 1+ 2, 1995

[12] Steg, E.M., Gedragsverandering ter vermindering van het autogebruik.Theoretische analyse en empirische studie over probleembesef,verminderingsbereidheid en beoordeling van maatregelen, 1996

[13] Schafer, A. & Victor, D., The past and future of global mobility, In:Schientific American, Oktober 1997, p.36-39, 1997

[14] Hupkes, G., Gasgeven ofafremmen, 1977[15] Zahavi, Y., Travel characteristics in cities of developing and developed

countries, 1976


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