A Comparison of Short Distance Transport Modes M.E. Bouwman Center for Energy and Environmental Studies IVEM University Ofgronin

A comparison of short distance transport modes

M.E. Bouwman

Center for Energy and Environmental Studies IVEM University ofGroningen Nijenborgh 4

9747 AG Groningen The Netherlands

Abstract

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

1 Introduction

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

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There is a wide variety of options for energy conservation in transport. One of the energy saving options is to switch to modes that have a relatively low energy consumption. This paper focuses on the assessment of transport modes with a minimised environmental impact per travelled kilometre. The first question arising is how to determine what mode has the smallest environmental impact, or in other words, how to rank the available transport modes according to an increasing environmental impact per travelled kilometre. Besides, it is interesting to determine whether such a ranking order changes under varying circumstances.

This paper discusses whether the transport mode with the smallest environmental impact can be identified, and the sensitivity of the environmental ranking order for specific variables, such as the spatial setting. Section 2 discusses which characteristics of transport modes are relevant in determining the environmental ranking of the modes. Section 3 shows in more detail the differences between the various spatial settings, and why this subdivision is worth looking at in more detail. Section 4 discusses the method used and section 5 presents some results.

2 Transport modes

For inner-city transport, a variety of transport modes is available. In general, the variety of transport modes is bigger in larger cities than in smaller ones, as public transport systems are more profitable in bigger cities. From an environmental point of view, it can be interesting to investigate whether energy savings are possible by changes in the modal split. From a variety of recent research reports (e.g. Steg [12]), one may conclude that changing the current modal split is not easy. So, before trying to influence the modal split, it is important to see which modes are environmentally preferable, 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 that influence 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 environmental impact, 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 other environmental problems associated with transport, such as noise and stench. Speed is an important transport mode characteristic. It appears that individuals have a relatively fixed time budget that they can spend on transport (e.g. Schafer [13], Hupkes [14], and Zahavi [15]). This budget comprises about one hour to one hour and a half a day. By increasing the travel speed, the individual mobility can be expanded without exceeding the travel time budget. For costs, a similar reasoning is valid. Households spend on average about 15 percent of their budget on transportation (CBS [9]). Especially for individuals with low incomes, cheaper modes might imply an expansion of their opportunities.

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In mode choice, other variables play a role too, such as reliability, comfort and habits. These characteristics are less easy to quantify than costs and speed of modes. Therefore, costs and speed are used as indicators of the personal preferences, based on the idea that cheaper and faster modes can contribute to a higher personal mobility, which seems to be an overall goal of many individuals. Based on these considerations, an analysis will be performed on the possibility of ranking various modes, based on four variables. Two variables, space use and energy use, represent the environmental score of the modes. On a societal level, it is preferable to minimise these issues. Two other variables, costs and speed of modes, represent important characteristics of modes for an individual. In the analysis, all modes will have different scores on each of these four variables. By comparing these scores, modes can be distinguished.

3 Spatial settings

The described important variables of transport vary largely among various spatial settings. Not only do patterns of mobility vary with various spatial settings; the characteristics of transport modes may also change in different circumstances. Large cities are often associated with environmentally attractive mobility patterns. 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 to travelling by passenger car. This expectation is largely confirmed when looking at 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 strongly urbanised (more than 2500 dwellings per square kilometre) and a rural (less than 500 dwellings per square kilometre) situation in the Netherlands in 1996.

Table 1. Modal split in the Netherlands, 1996 (km/day/;lerson)

Very strongly urbanised Rural Passenger car - Driver 12.15 km 37.8% 18.20 km 51.6 % Passenger car - Passenger 7.79 km 24.2% 9.66 km 27.4 % Public Transport 7.42 km 23.1% 2.90 km 8.2% Moped 0.18 km 0.6% 0.28 km 0.8% Bicycle 2.92 km 9.1% 2.66 km 7.5%

Walk 1.17km 3.6 % 0.66 km 1.9 % Other 0.56 km 1.7% 0.92 km 26% Total 32.18 km 100 % 35.29 km 100 % Source: (CBS [7])

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

between very strongly urbanised settings and all other settings is bigger than the difference between two other settings. Table 1 shows a clear difference in the modal split of the two situations. In rural areas about 75 per cent of all kilometres are travelled by passenger car compared to about 60 per cent in compact cities. The difference of 15 per cent seems to be travelled on foot or with public transport in the compact city, which seems to correspond with the expectations indicated earlier. The observed mobility differences cannot be attributed fully to the differences in population density. Income is also a contributing factor for the observed differences in the modal split. The use of public transport in a compact city may be higher due to a greater access to this mode of transport. The share of public transport might also be lower in rural areas, because people who can afford to own a car prefer to live in rural areas. This second argument should correspond with clear income differences in the various situations, as it is known that car ownership levels are strongly correlated with income levels (see for example Korver [3]; CBS [10]). Table 2 gives information about different characteristics of households in various spatial settings, as well as their access to private transport.

Table 2. Household size, income, car ownership and use in different spatial settings Urban density vsu SUR URB WUR RUR Average household size (person/household) 2.19 2.41 2.54 2.69 2.73 Average standardised income (Dfl * thousand /year) 30.2 32.0 326 33.0 322 Average car ownership level (cars/household) 0.69 0.89 1.02 1.06 1.12 Share of households without a car (%) 39.9 25.0 17.5 15.3 12.9

Average annual kilometrage of a passenger car (km/year) 15,540 15,640 16,010 17,040 16,750 Average daily distance travelled (km/day/person) 32.18 34.11 34.43 35.02 35.29

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

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

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urbanised, and decreases in the rural case. The latter largely corresponds with the difference in household size; the average income is similar in both situations, but the larger average household size corresponds with a lower standardised average income in the rural areas. Car ownership (number of cars per household, as well as share of households with a car) increases with decreasing population densities. All the aspects referred to in table 2 contribute to the observed differences in mobility. It is generally agreed on that changes in income and household explain about two third of the variation in mobility, and that the last third is explained by differences in spatial settings.

4 Model description

A model is developed and used to calculate the characteristics of the various modes. Seven transport modes are distinguished: three passenger cars (fuelled with 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 lengths is 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-up area. The model is based on the Dutch situation, and uses 1996 as the base year for data input. Calculations can be made for the period 2000 - 2020. Calculations are 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 and the very strongly urbanised setting. The latter represents the mobility of the inhabitants of the thirteen biggest cities of the Netherlands. Characteristics of the transport modes for the year 2000 are based on the historical fleet data and the corresponding technical characteristics. For calculations in future years, the various fleets are updated annually by replacing the oldest vehicles with newer ones. The calculated energy use not only depends on 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 the energy use. For calculating the energy use of the various modes, not only the direct energy use is taken into account. Also the indirect energy use associated with the production and maintenance of vehicles and infrastructure is included in the analysis. The total energy use of these two contributing components is ascribed to the total number of kilometres driven with or on it.

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

420 Urban Transport and the Environment for the 21st Century

Mobility

demand

Vehicle characteristics

Figure 1. Model structure

The space use is defined by the amount of infrastructure and the total mobility demand. The more the infrastructure is used, the lower the space use per travelled 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 not offer enough capacity, congestion will occur which increases the travel time.

The results are presented per kilometre. However, in comparing the results of the various modes, an extra correction is needed. Most individual modes can be used from destination to destination, while for public transportation often a detour 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 detour can become pretty large. The detour factors used are shown in table 3.

Table 3. Detour factors

Transport mode Trips under 10 km vsu RUR Petrol passenger car 1.1 1.0 Diesel passenger car 1.1 1.0 LPG passenger car 1.1 1.0 Bus, tram, metro 1.2 1.2 Train 1.3 1.3 Bicycle 1.0 1.0 Walk 1.0 1.0 VSU: Very strongly urbanised, RUR: Rural

5 Model results

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

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

Table 4. Model results for 2000, the Netherlands, trips <10 km Transport mode Veiry strongly urbanised Rural S E T C S E T C Petrol passenger car 0.8 2.8 1.8 0.5 0.6 2.4 1.5 0.5 Diesel passenger car 0.8 2.7 1.8 0.3 0.6 2.3 1.5 0.3 LPG passenger car 0.8 2.8 1.8 0.3 0.6 2.4 1.5 0.3 Train 0.5 0.9 0.9 0.3 0.5 0.9 0.9 0.3 Bus, tram, metro 0.9 2.1 2.0 0.3 0.8 2.0 1.8 0.3 Bicycle 0.8 0.0 4.5 0.1 0.8 0.0 4.3 0.1 Walk 0.8 0.0 12.8 0.0 0.8 0.0 12.7 0.0 Legend: S = space use (10 m'Vkm), E = energy use (MJ/passenger km), T travel time (min/km), C = costs (Dfl/km)

Note that even with the detour factors, the results cannot be interpreted unambiguously. Even with the restriction of the distance of trips to ten kilometres, not all modes can be equally used. Walking is only suited for the shorter trips in this category, while the train can only sometimes be used at the upper end of the trip lengths. Bus, tram and metro are not an alternative for very short trips. Passenger car and bicycles can be used over the whole distance range of trips below ten kilometre. These restrictions on the use of modes should be combined with the fact that public transport systems generally extra transport to arrive at and depart from the stations and bus stops. This should be taken into account in interpreting the results. The various passenger cars have values that only differ in costs. This can be explained by the Dutch tax system, which makes diesel and LPG cars only interesting in case of high annual use. Both car types have an annual use of over twice the annual use of a petrol car. This results in dividing the fixed costs over a greater number of kilometres. Next to that, the fuel concerned is cheaper. The space use is based on the area used and the amount of kilometres travelled on the infrastructure. For this reason, space use is relatively low for highways, on which a large amount of kilometres are travelled. Bus, tram and metro score relatively bad, because next to the normal share they have in general roads, also the surface area of specific infrastructure like rail and bus lanes is distributed to these systems. The soft modes walking and cycling also have relatively high space use. The total area comprised by sidewalks and bicycle lanes is not very big, but the amount of kilometres travelled on this infrastructure is also relatively small. One may question however, whether the full space use of sidewalks should be ascribed to the function of walking, as these are also used for other functions. The travel time is based on average calculations. This means that effects of congestion are distributed over all trips made. Although some trips will face considerable delay due to a lack of capacity of the infrastructure, the overall effects can hardly be noticed. The travel time shows large differences between the modes as well as between the spatial settings. Passenger cars face considerably more delay in an urban environment than in villages, generally

422 Urban Transport and the Environment for the 21st Century resulting in a higher travel time value. The good score for the train is due to the fact 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 be used as a sole mode, like mentioned above, and should be combined with other modes, in order arrive at and depart from railway stations. Train use on short distances is not very likely. The soft modes walking and cycling differ from the other modes by relatively long travel times. Apparently, muscle force poses some 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 of faster roads, such as highways. Energy use and accompanying costs for passenger cars are higher in case of urban transport. Table 5 displays the order of the results. Below, the modes are classified for each of the four variables. In this way, the information on the differences in scores on the variables is lost, but the information is easier to interpret. The ranking scores on the various variables can be added to get one overall score of the modes, again, without an indication on the underlying differences.

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

Transport mode Very strDngly urbanise;d Rural S E T TOT S E T C TOT Petrol passenger car 3 7 3 7 20 3 7 3 20 Diesel passenger car 3 5 3 6 17 3 5 3 6 17 LPG passenger car 3 6 3 5 17 3 6 3 5 17 Train 1 3 1 3 8 1 3 1 3 8 Bus, tram, metro 7 4 5 4 20 7 4 5 4 20 Bicycle 5 2 6 2 15 5 2 6 2 15 Walk 6 1 7 1 15 6 1 7 1 15

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

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

In order to present results for coming years, assumptions should be made on the development of the mobility demand, the population size, the technical characteristics of the vehicles and the changes in the available infrastructure. It is assumed that the population will develop according to the mid-scenario presented 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].

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This database shows a greater potential for efficiency for passenger cars and busses than for trains. The overall length of the road infrastructure is assumed not to grow with the exception of the roads within the built-up areas for the opening up of new residential areas. Results of the analysis with these assumptions are shown in table 6.

Table 6. Model results for 2020, the Netherlands, trips < 10 km Transport mode Vei•y strongly urbanised Rural S E T C S E T C Petrol passenger car 0.6 2.1 1.8 0.4 0.5 1.8 1.5 0.4 Diesel passenger car 0.6 1.9 1.8 0.3 0.5 1.6 1.5 0.2 LPG passenger car 0.6 2.5 1.8 0.3 0.5 2.1 1.5 0.2 Train 0.4 0.9 0.9 0.3 0.4 0.9 0.9 0.3 Bus, tram, metro 0.7 1.3 2.0 0.3 0.6 1.2 1.8 0.3 Bicycle 0.6 0.0 4.5 0.1 0.6 0.0 4.3 0.1 Walk 0.6 0.0 12.8 0.0 0.6 0.0 12.7 0.0

Legend: S = space use (1(T nr/km), E = energy use (MJ/passenger km), T travel 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 efficient passenger car in that time, as still older cars are also present in the fleet. For this reason, in the short term bigger efficiency improvements can be achieved for road vehicles than for trains, as trains have a longer lifetime. The space use has generally declined, since no road extensions were made, while the total mobility increased.

Table 7. Ranked results for 2020, the Netherlands, trips < 10 km Transport mode Very stnDngly urbanised Rural S E T C ITOT S E T C 1 TOT Petrol passenger car 3 6 3 7 ! 19 3 6 3 7 1 19 Diesel passenger car 3 5 3 4 ! 15 3 5 3 4 1 15 LPG passenger car 3 7 3 3 ! 16 3 7 3 3 ! 16 Train 1 3 1 5 1 10 1 3 1 5 ! 10 Bus, tram, metro 7 4 5 6 I 22 7 4 5 6 1 22 Bicycle 6 2 6 2 ! 16 6 2 6 2 1 16 Walk 5 1 7 1 1 14 5 1 7 1 1 14

Table 7 shows the results of table 6 classified to order. It shows clearly the improved position of the passenger car compared to the other modes, due to the relatively 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 transport modes. A dynamical analysis can add important information, as the order of the transport modes can change due to the introduction of new technologies.

424 Urban Transport and the Environment for the 21st Century

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

Acknowledgements

The author likes to thank Henk Moll, Rene Benders and Ton Schoot Uiterkamp for 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 social

dimensions 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 Nederlandse bevolking in 1994, 1995 [5] Centraal Bureau voor de Statistiek, Stand en loop van de bevolking per gemeentegroep naar stedelijkheid gemeenten, 1993, In: Maandstatistiek van 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 verplaatsingsgedrag 1996, 1997 [8] Centraal Bureau voor de Statistiek, Regionale inkomensverdeling 1994.

Kerncijfers, 1996 [9] Centraal Bureau voor de Statistiek & Kluwer Voertuigtechniek, Auto's in 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