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QUANTIFYING THE POTENTIALS OF A NEW HIGH SPEED TRAIN USING
A GRAVITY MODEL AND GIS
Simone Ehrenberger, Joachim Winter, Fabian Malik
German Aerospace Centre, Institute for Vehicle Concepts, Stuttgart, Germany
1 Introduction
Mobility is an important part of today’s life. It is not only a necessity for each
individual, economic progress also depends on a highly developed
infrastructure. In the past years, high speed rail (HSR) has become more and
more important in infrastructure policy. HSR promises an energy efficient, safe
and comfortable way of travelling. During the past years, new high speed rail
lines have been built in several countries. As leading country, China has built
6,500 km of HSR tracks up to now and is planning to expand its network to
13,000 km until 2012 [1].
From a political point of view transportation and transport infrastructure plays
a central role for social and economic development. In the last years,
transport policy is closely connected to energy policy and the global aim to
reduce carbon emissions. Driven by these environmental targets, rail-bound
transportation has experienced a revitalisation in numerous countries in the
past few years. Especially HSR benefits from this development. With a
considerably low energy consumption of high speed trains per kilometre and
seat in comparison with airplanes, expanding HSR networks will support
development towards a more environmental friendly transportation. Future
HSR transportation requires vehicles with efficient energy usage while
matching travellers’ demand of high comfort and low travel times. The
German Centre for Aerospace (DLR) develops high speed trains in its
research framework “Next Generation Train” (NGT) aiming to deliver train
concepts capable to meet the technical and environmental requirements of
coming decades.
When realising a new train concept, one key question is where such trains
could be operated and which routes would provide the greatest potentials.
This paper presents a gravity model which calculates passenger numbers on
potential European HSR routes. For routes outside Europe, a simplified
approach based on air passenger capacities has been used to estimate traffic
volumes. Based on the results of these two rail passenger models, we
calculate the number of required NGT vehicles and operating costs using
specific parameters that determine the operation of HSR lines. The
investigated routes can be categorised by means of expected profitability.
Additionally, we evaluate the feasibility of HSR routes by considering
geographic influence factors. Based on a geographic information system
(GIS), a HSR route model calculates the most cost-effective path between two
cities. Parameters as terrain slope, population density or water bodies
influence the course of a HSR track and its constructions costs significantly.
© Association for European Transport and contributors 2010
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2 The Next Generation Train Project
Since 2007, nine DLR research institutes have been working on the
development of the NGT. The main objective of this project is to provide
research results from aerospace to the railway industry in order to face upcoming transport tasks.
The properties of the NGT are well defined by a systems requirement
specification, gathering requirements of potential customers and the relevant
European standards for such a train. Herein a high capacity high speed train
is described. The mirrored proposal specification describes the DLR vision for
such a product.
High operational flexibility is a main feature of the NGT. To support a simple
maintenance concept and easy ex-change of a defective coach, the single car
principle is applied. Each coach can drive by itself. This feature has big
advantages for marshalling and maintenance. A full-length double deck
electrical multiple unit (EMU) is built of 8 middle coaches and 2 power heads
giving a total length of 202 m (figure1). It provides seats for up to 790
passengers in two classes including an onboard restaurant, compartments for
parents with children and handicapped persons. Via an optical coupling
several train sets can be coupled.
The double deck coaches are fully continuous on two levels. The passengers
can enter and exit the train on both levels. Therefore the EMU has no stairs.
The passenger baggage is handled separately by a baggage system in the
power heads. To achieve short passenger exchange times the door concept
and the interior of the coaches are verified by a passenger flow analysis.
The operational flexibility is furthermore improved by the opportunity to split
trains dynamically. That means trains can optically couple and decouple
during runs. After introducing the flexible block as a train safety principal, it will
be possible to increase the line through-put.
The power supply is assumed trendsetting as track integrated. Thus, the
maintenance intensive catenary including the chain net diminishes. The
propulsion concept shows over the whole EMU length a distributed contactfree power input from the trackside. On the vehicle side there is no longer the
noisy and heavily wear pantograph. The wear and noise of the running gear
are reduced by using a mechatronic wheel set. This is realized as a radial
controllable differentially powered single wheel single running gear which
steers the wheel actively into a curve. Further technological innovations are
related to the train’s lightweight design and its aerodynamic optimization.
Figure 1:
Design study of the Next Generation Train (NGT)
© Association for European Transport and contributors 2010
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The power heads of the NGT are delivering about 50% of 18 MW drive
propulsion. The remaining drive propulsion is delivered by wheel hub motors
of the single wheel single running gear. The acceleration of the NGT is
therefore significantly above average. The double deck high speed EMU has
a scheduled speed of 400 km/h and will be certified for 440 km/h.
Compared to today’s high speed trains like the German ICE 3, the NGT has
significant advantages in terms of low specific energy consumption, low noise,
comfortable air conditioning, optimised passenger flow and also low wheel/rail
wear. The NGT combines high comfort for passengers with low travel times.
Both play a major part for the competition between rail and air traffic on short
and middle distance journeys.
Air – rail competition – How to substitute air traffic by high speed
trains
The criteria for the decision for or against a certain transport mode are mainly
travel time, costs, comfort and the reliability of transportation means.
Considering the competition of rail and air traffic, the accessibility of terminals,
value of time and quality of service are additional parameters that determine
the success of a certain transport mode [2]. Studies on air-rail competition
show that about 90 % of the differences in market share could be explained
by evaluating travel time, check-in time and schedules [2]. Significantly high
correlation can be observed for travel time and market share. For rail trips of
less than one hour, market shares usually are more than 70 %.
The time which is relevant for the traveller’s decision consists of time of
travelling from one city to another plus access time to the train or the airplane,
respectively.
Figure 2 shows travel times for rail and air transportion. We assumed that an
average airport can be reached within 30 minutes and travellers have to be at
the airport 45 minutes in advance for check-in, security checks and waiting for
boarding. Trains in general are faster to access. In figure 2, the access time is
15 minutes for the drive to the main station and the boarding to the train. The
figure depicts travel times for the NGT, a train with an average speed of
200 km/h and airplanes with an average speed of 500 and 700 km/h. For rail
travel, speed is much more important than for airplanes. On the distances
investigated, the planes need a considerable share of time for departure and
landing. For trains, however, speed is a decisive parameter as the faster it is
the farer it gets within a certain range of time.
Under these circumstances and due to the driving characteristics of the NGT,
distances up to about 800 km provide time savings compared to airplanes.
Beyond this, air traffic is usually faster than rail. Thus, for the calculations in
chapters 4 and 5 the length of all selected routes is less than 800 km as we
consider longer distances to be less competitive on a specific route than air
transport.
3
© Association for European Transport and contributors 2010
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6
Travel Time [h]
5
4
3
2
1
0
100
200
300
400
500
600
700
800
900
1000
Travel Distance [km]
NGT
Figure 2:
HSR - 200
Air Traffic - 700
Air Traffic - 500
Travel time of air traffic and HSR
The target of this approach is to classify potential HSR routes according to
their suitability for the operation of the new NGT concept. Thus, we identified
potential city links in different geographical areas and estimated operation and
infrastructure costs in order to evaluate the potential for the NGT on each
specific route.
The evaluation of potential lines for operation of a new future train concept
consists of several steps. First, we identified potential high speed lines. City
links in the area under investigation have been chosen by searching lines
between cities with more than a defined number of inhabitants and within a
defined range of kilometres. Concerning the number of inhabitants, we only
included cities with more than 500.000 inhabitants for all areas except from
China, where we defined a limit of one million inhabitants for reasons of
simplification.
In a second step, we estimated travel demand using a gravity model which
calculates numbers of HSR passenger for European routes. For countries
outside Europe, we derived potential HSR passengers from travellers using
air transport. We assumed that a certain amount of travellers will switch to
HSR and that new HSR lines produce additional traffic.
The success of HSR also depends on political decisions on investments in
HSR infrastructure. More than air traffic, HSR depends on an appropriate
infrastructure when operating new routes. For the evaluation of possible
routes in terms of the technical feasibility and infrastructure costs, we
developed a model based on GIS. It calculates the cost effective path
between two cities. The information on such routes provides an additional
decision tool for the evaluation of HSR lines.
© Association for European Transport and contributors 2010
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4
Europe
4.1 Configuration of the Gravity Model
The calculation of rail passenger numbers throughout Europe requires a
macroscopic approach that analyses the relation between expected traffic and
other parameters as, for instance, the population within an area. Thus, we
used a linear regression analysis to develop a model for rail traffic flows
between two places in terms of trips per year.
Generally, travel volume of an area depends on the relation between transport
supply and socio-economic attributes that influence travel demand [3].
Transport supply is described by availability of rail or HSR and its
characteristics, e.g. speed. Travel demand is influenced by the intention of a
trip. Typical motives for travelling are work, business purposes, education or
shopping. These motives correlate with indicators that can be used to
calculate the number of trips on a specific route. Population size of a city and
number of working places are typical indicators. The simple linear regression
analysis evaluates the quality of the correlations between certain indicators.
For HSR, the main travel motives are business and leisure. Possible
indicators to evaluate these activities are the gross domestic product (GDP)
and intensity of tourism. A place with high GDP produces high travel demand,
but also attracts travellers from other places. Intensity of tourism represents
the attractiveness of a place to visitors from other places. As this group of
travellers come for a visit, but then travel back home again, the traffic
produced by this indicator counts for two directions. A third indicator is the
size of population of a city.
In a first step, we evaluated the correlation between these indicators by using
data on rail and air traffic between 13 cities in Germany with more than about
500,000 inhabitants and a distance of more than 150 km. Using the linear
regression analysis, we determined the correlation between each indicator
and traffic volume on the city links. The dependencies of indicators and traffic
data are statistically analysed by using the correlation coefficient (r) by
Pearson which indicates the strength of a correlation between two
parameters. We calculated the regression line and analysed its quality by
calculating the coefficients of determination (R²).
For both rail and air traffic, the correlation coefficient and the coefficients of
determination are highest for travel volume and GDP. Population size is also
highly correlated to traffic volume. For intensity of tourism, results show a high
correlation for air traffic. But for rail traffic, this indicator is not suitable for
estimating traffic volumes.
A model for the generation of traffic represents the entire traffic of a certain
area, but not the distribution of traffic within this area. Thus, in a second step
we developed a gravity model that calculates traffic flows between two cities.
Apart from socio-economic factors, the gravity model takes external factors
into account, such as distance between cities in order to explain interactions
between areas.
The first gravity model used for the examination of travel demand and
distribution has been developed by the Austrian Lill in 1891. The gravity
models used in transportation research can basically be described as:
© Association for European Transport and contributors 2010
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Fij  
Pi  Pj
d ijb
Fij represents the number of trips between the places i and j. d is the distance
between these places and b is a weighting exponent for d. β is an empiric
constant factor. P is the mass factor of i and j, which can be described with
population size or GDP, for instance [4].
In this equation, the distance represents a resistance coefficient for travelling
on a specific route. But distance is an insufficient measure for describing the
time and effort for travelling from one place to another. The resistance
coefficient should also include travel time and average velocity [5].
For the development of a gravity model that couples parameter of two cities,
we chose the indicators evaluated by the regression analysis for the
calculation the traffic volume: population size P, GDP W, and intensity of
tourism TI. Additionally, we implemented the distance dij between cities and
the travel time tij to for a certain route. The traffic volume between city i and
city j results from:



Fij   0  Pi  Pj  1  Wi  W j  2  TI i  TI j  3  d ij4  tij5
This equation represents a multiple regression model with several
independent variables. We calibrated this equation using again data on rail
travellers between 13 cities in Germany with more than 500,000 inhabitants.
For the determination of the optimal β coefficients, we tested variations of the
equation above. GDP and population size have been proved to be good
indicators for traffic volume, as they show high statistical relevance. Statistical
tests show, however, that the intensity of tourism is related to a high
probability of error. For the German data, the share of traffic volume due to
the intensity of tourism is with a probability of 94 % a random result. Thus, the
gravity model is reduced by the variable TI. The resistance coefficients dij and
tij show a high multi-collinearity. These two indicators influence each other, but
distance has proved to be highly significant and travel time gives information
on travel speed. As a consequence, we combined distance and travel time to
one resistance coefficient. The final equation for the calculation of travel
demand is the following:
Fij   0  Pi  Pj  1  Wi  W j 

2


 d ij 


 v ij

 100 
3
This gravity model is applied to potential European HSR routes as described
in the following chapter.
4.2 Analysis of HSR passenger potential in Europe
For the analysis of potential passenger volumes in Europe, we assumed that
the relationship between the socio-economic parameters, distance, travel time
and traffic volume is basically the same in every country. For the calculation of
the potentials, all cities within EU 27 as well as Norway and Switzerland with
more than 500,000 inhabitants have been included. As a maximum railway
© Association for European Transport and contributors 2010
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distance, we considered 800 km. The average factor for deviation from linear
distance for railways in Europe is 24 %. Thus, an 800 km railway route is
equal to 645 km of linear distance.
The 63 European cities included in the model lead to 401 connections for
which the passenger volumes have been calculated. Figure 3 depicts the
relative distribution of start and end points per country in relation to the
absolute number of selected cities in each country. Countries in central
Europe, especially Germany, Belgium and the Czech Republic, are obviously
preferred for HSR routes. Regarding a possible European HSR network,
these countries would serve as nodes. Among the 401 connections, there are
142 national and 259 international routes. The national connections generally
are related to higher traffic volumes than international ones as the gravity
model calculates with slightly higher resistance coefficients when a route
passes a border. This results from linguistic, cultural and economic obstacles
that are higher compared to national connections.
The resistance for international trips decreases with increasing rail speed. If
average travel speed increases by 20 %, passenger numbers on national
connections will grow by 27 %. For international connections, the estimated
number of passengers will increase by 34 %.
Figure 3:
Relative share of European countries regarding HSR
connections
The left side of table 1 shows the 25 European connections which are most
frequented for an average speed of 200 km/h. This represents the maximum
present average speed for European HSR lines. The most frequented route is
London – Birmingham followed by London – Paris. As for the NGT the
operation speed and thus the average velocity would increase, the right part
of table 1 lists passenger volumes for an average 300 km/h speed. In that
case, the two best connections appear in reversed order. Generally, distances
of less than 300 km show a remarkable share in the results. Other
connections have a maximal distance of 500 km. Assuming an average
deviation to linear distance of 24 %, this is equal to 370 or 620 km,
respectively. Under ideal conditions, these distances correspond for the NGT
© Association for European Transport and contributors 2010
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to a maximum of about 3 hours travel time which can be considered as a
maximum travel time especially for business trips. Nevertheless, the model
seems to underestimate passenger volumes on larger distances. The
connection Barcelona – Madrid, for instance, does not appear in the
European top 25 routes though it is one of the most promising connections in
Europe. This underestimation results from on German data which are mainly
based on rail distances less than the 744 km of Madrid – Barcelona.
Table 1:
25 highest passenger potentials at 200 km/h and 300
km/h average speed
Routes
Birmingham<->London
London<->Paris
Paris<->Lille
Paris<->Lyon
Hamburg<->Berlin
Cologne<->Frankfurt
Stuttgart<->Frankfurt
Stuttgart<->Munich
Leeds<->London
Dusseldorf<->Frankfurt
Paris<->Bruxelles
Munich<->Frankfurt
Sheffield<->London
Nantes<->Paris
Frankfurt<->Hamburg
Dusseldorf<->Hamburg
Frankfurt<->Nuremberg
Cologne<->Hamburg
Dresden<->Berlin
London<->Bruxelles
Frankfurt<->Berlin
Bordeaux<->Paris
Munich<->Berlin
Essen<->Frankfurt
Stuttgart<->Nuremberg
Distance
[km]
165
344
204
394
256
153
153
190
275
183
265
304
229
345
393
338
188
357
165
320
425
502
505
190
157
Passengers
Ø 200 km/h
8.766.461
8.096.654
7.430.446
5.287.699
4.986.513
4.523.267
4.310.921
3.938.020
3.795.074
3.761.662
3.385.630
3.084.028
2.611.477
2.574.947
2.447.696
2.280.088
2.033.722
2.013.050
1.957.335
1.946.581
1.890.995
1.886.497
1.840.888
1.771.908
1.771.342
Routes
London<->Paris
Birmingham<->London
Paris<->Lille
Paris<->Lyon
Hamburg<->Berlin
Cologne<->Frankfurt
Stuttgart<->Frankfurt
Stuttgart<->Munich
Paris<->Bruxelles
Leeds<->London
Dusseldorf<->Frankfurt
Munich<->Frankfurt
Sheffield<->London
Nantes<->Paris
Frankfurt<->Hamburg
Dusseldorf<->Hamburg
London<->Bruxelles
Frankfurt<->Nurnberg
Cologne<->Hamburg
Dresden<->Berlin
Frankfurt<->Berlin
Bordeaux<->Paris
Munich<->Berlin
Essen<->Frankfurt
Stuttgart<->Nuremberg
Distance
[km]
344
165
204
394
256
153
153
190
265
275
183
304
229
345
393
338
320
188
357
165
425
502
505
190
157
Passengers
Ø 300 km/h
15.534.033
14.922.996
12.648.720
9.001.160
8.488.456
7.699.881
7.338.406
6.703.624
6.495.584
6.460.289
6.403.413
5.249.888
4.445.472
4.383.288
4.166.671
3.881.355
3.734.660
3.461.970
3.426.781
3.331.938
3.219.008
3.211.351
3.133.712
3.016.289
3.015.325
The comparison of the results with recorded passenger volumes shows that
the gravity model under- or overestimates the traffic volumes on some
connections with less than one million passengers per year by more than
25 %. As the model has a high statistical quality, these differences cannot be
explained by statistic errors of estimation, but by factors that are not included
in the gravity model. Such factors are, for instance, the quality of existing
infrastructure on a region, competition to road or air traffic and political or
economic exceptional positions of some cities. In other cases where a city is
not connected to a HSR network at present, the model considers passenger
potentials for HSR that can be attracted in the future.
4.3 Operating Costs
The success of a HSR line depends on several influencing factors. One
determinant is the number of passengers that are expected to travel on a
specific route. As shown above, traffic volume depends on distance and
operation speed. These factors also determine the options for operation of a
rail route and its overall operating costs.
Operating costs show the relation between the required number of trains for a
certain number of passengers and distance of a route. Thus, the routes
© Association for European Transport and contributors 2010
8
resulting from the gravity model can be classified according to their economic
performance.
Due to the high number of routes, we exclusively considered the route
characteristics mentioned above for the estimation of operating costs. We
calculated the minimum travel time under ideal conditions regarding
infrastructure for the NGT. From this, we obtained the minimum number of
trains necessary to transport the expected number of passengers from one
city to another. Using this information, we calculated the total kilometres
travelled by each train per year, its energy consumption and the required
personnel hours. Another important cost factor for train operators are
infrastructure charges. These costs are represented by using a general
infrastructure charge per kilometre which is similar to that on German high
speed routes.
The revenues are calculated based on general ticket prices per kilometre, as
a linear increase of ticket prices would lead to an overestimation of revenues
on long distances. In practice, prices per kilometre on long distances are
lower than on short distances for rail travel as well as air traffic. The model
addresses this by reducing the ticket fees gradually for growing distances.
The profitability of potential European routes has been assessed by
calculating the ratio of revenues from ticket sales and operation costs. The
results have been divided into three groups. Route category 1 represents the
first third of the results showing the highest benefits, category 3 contains the
lowest third. Figure 4 depicts the distribution of the European routes in relation
to rail passengers and distance. Revenues as well as costs depend both on
traffic volume and distance. Generally, high passenger numbers and low
distances are most beneficial. Most of the category 1 routes have a distance
less than 500 km and a traffic volume of more than 500,000 passengers per
year. For distances of more than 600 km, there is only one route with 1 million
passengers that belongs to the top 30 % routes regarding revenue-cost ratio.
Passenger numbers less than 100,000 per year generally lead to a less
promising HSR route.
700
600
Distance [km]
500
Category 1
Category 2
400
Category 3
300
200
100
1.000
10.000
100.000
1.000.000
10.000.000
100.000.000
Rail passengers
Figure 4:
Categorised operating costs in relation to rail passengers
and distance on European HSR routes
© Association for European Transport and contributors 2010
9
5 Traffic volume and cost estimations in countries outside Europe
In Europe, HSR has been an alternative to air traffic on many routes for
several years. The HSR network is still growing in there, but potentials for
HSR are considered high in other countries as well. In the following, we
present an evaluation of promising routes in China, Brazil and the India. The
method of our analysis is similar to our studies on European routes described
in chapter 4. There is one main difference in our calculations: instead of using
a gravity model we used a simplified approach for the calculation of potential
rail passengers on each route.
For first estimations on potential rail travel demand in other countries outside
Europe, we analysed city links that fulfilled the requirements described in
chapter 4.3. We included cities with more than 500,000 inhabitants, only for
China cities smaller than 1 million inhabitants were excluded. As we have no
information on possible factors for deviations from linear distance for countries
outside Europe, we disregard this factor and include all routes up to 800 km.
Based on flight seat capacities in 20091, we calculated the number of rail
passengers assuming a percentage of modal shift that depends on travel time
or the distance travelled, respectively. According to estimations by the
International Union of Railways (UIC) [6], we assumed that the HSR share
exceeds 50 % for journeys less than 800 km compared to air travel. A
distance of less than 600 km is supposed to lead to a modal shift of 70 % and
routes with less than 400 km length to a shift of 85 to 100 %. Further, we
considered additionally induced travel demand (passengers who would not
travel without existing HSR connection).
For the calculation of operating costs, we used the same approach as
described in chapter 4.3 for European routes. The specific cost factors differ
from country to country.
Furthermore, based on data from the US Bureau of Transportation Statistics
[7], we examined connections for US cities using the same methodological
approach.
Figure 5 depicts the results exemplarily for Brazil. For 20 out of 24 possible
connections direct flights were available in 2009. The right map shows
passenger data that provides the basis for the calculation of the required
number of trains and operation costs. The potential HSR lines in Brazil are
divided into two regions: one network in the north-eastern part and another in
the south. Due to city sizes and a relatively short linear distance of about
350 km, the connection Sao Paulo – Rio de Janeiro stands out. Estimated
passengers between these cities account to 6.7 million per year. Other
connections to these cities are relatively high frequented as well. In the northeastern region, the connection between Recife and Salvador constitutes the
highest numbers of passengers. The map to the left in figure 5 outlines the
calculated operating costs categorised into three groups. Category 1
represents those tracks showing the highest revenue-cost ratio. The southern
part of Brazil shows the most routes of category 1. Distances between cities
as well as passenger numbers are here more promising than in the northeastern part of the country. From figure 5 it becomes apparent for further
planning of a Brazilian HSR network that passenger numbers will add up on
1
The data on flights in 2009 of the Official Airline Guide Database have been kindly provided
by the DLR Air Transport and Airport Research Institute.
© Association for European Transport and contributors 2010
10
certain routes. For example, it is reasonable to build the connection between
Curitiba and Rio de Janeiro via Sao Paulo. Capacity utilisation would improve
for the sections of the route while loss of travel time remains small. Moreover,
construction costs for infrastructure can minimised.
Figure 5:
Potential HSR lines in Brazil
Passenger numbers play a distinctive role for the classification of investigated
connections. Another salient aspect is the distance between cities. As
described for Europe, distance not only influences passenger numbers
through the required travel time, but also determines operational
specifications for each route. Table 2 provides an overview of the number of
connections analysed, the number of passengers and the minimum number of
trains that are required to operate the HSR routes in each country. In total, at
least 230 NGTs would be required to operate the investigated connections.
Figure 6 displays the relation between annual passenger numbers and
distances between the investigated cities in Brazil, China, India and the USA.
With respect to passenger numbers, connections in the USA on average
depict the highest numbers. Distances between the investigated cities in the
US are often in a median range which is highly beneficial for HSR in
competitions to air traffic. The analysed connections of cities in India show
generally shorter distances, but also less passenger volume. This result in a
smaller number of vehicles that are required to operate these routes
compared to the USA. A considerable share of cities in India is less than 500
km apart, a criterion which seems to be highly beneficial for the development
of a HSR network. Similar trends can be observed for Brazil, where distances
of less than 550 km are most beneficial.
The model for China on the other hand shows in general larger distances as
only cities with more than 1 million inhabitants are considered. Nevertheless,
the number of connections is significantly higher than in the other countries
(table 1). This leads to a higher number of required trains. With respect to the
© Association for European Transport and contributors 2010
11
number of routes and the size of cities, passenger numbers in China seem to
be relatively low which can be seen in figure 6. One reason for this
observation is that air traffic in China is not as well established as in other
countries. The potentials for HSR lines stem to a greater extent from
conventional railway transportation and not from air traffic. Therefore, at the
current state of the model, passenger numbers in China tend to be
underestimated in comparison to other countries.
Table 2:
Country
Overview of results for countries outside Europe
Number of
Routes
Brazil
China
India
USA
Minimum number
of trains
Number of rail passengers per year
Route Category 1 Route Category 2 Route Category 3
12855479
3.302.252
671.086
13.475.557
5.271.863
1.392.356
7.734.125
2.190.160
466.276
12.869.662
7.354.372
6.523.122
20
90
35
25
Brazil
38
102
38
52
China
800
800
700
700
600
600
Category 1
Distance [km]
Distance [km]
Category 1
Category 2
Category 2
500
Category 3
500
Category 3
400
400
300
300
200
200
100
100
0
1.000.000
2.000.000
3.000.000
4.000.000
5.000.000
6.000.000
7.000.000
8.000.000
0
200.000
Rail passengers
400.000
600.000
1.200.000
800
800
700
700
600
Distance [km]
600
Distance [km]
1.000.000
USA
India
500
400
Category 1
Category 1
Category 2
500
Category 2
Category 3
Category 3
400
300
300
200
202
200
100
100
0
200.000
400.000
600.000
800.000
1.000.000
1.200.000
1.400.000
1.600.000
Category 1
Figure 6:
0
500.000
1.000.000
1.500.000
2.000.000
2.500.000
Rail passengers
Rail passengers
800
800.000
Rail passengers
Category 2
Category 3
Categorised operating costs in relation to rail
passengers and distance on routes in Brazil, India,
China and USA
700
Distance
600
500
© Association for European Transport and contributors 2010
400
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6
Using GIS for Examining Railway Lines
6.1 Methodology
Apart from analysing potential passenger volumes as described in chapters 4
and 5, rail infrastructure has to be evaluated for the assessment of future HSR
lines. As the construction of rail infrastructure requires considerable
investments, the assessment of technical specifications of such routes
represents another important decision criterion. We examined the potential
HSR routes using a geographic information system (GIS). The methodological
approach for the determination of the most cost efficient path is exemplarily
described for the European routes in the following.
Construction costs for high speed tracks vary considerably within one country
as well as between countries. On the one hand these differences result from
different construction types. On the other hand railway tracks face different
geographic and geological conditions. In average the costs for the
construction of one kilometre HSR track are 18 million € [8].
For modeling the influence of geographic conditions on the course of railway
tracks, we defined cost or resistance factors for each parameter. Then, we
calculated resistance maps that assign relative resistance values to each
raster pixel of the map of Europe. This resistance value represents the factor
the basic costs for track construction have to be multiplied with. Basic costs
are those expected for the route construction under ideal conditions. Each
parameter leads to a resistance map. The maps are joined to a total
resistance map at the end of the process.
The parameters that increase construction costs or prohibit track construction
at all are: slope of the terrain, population density and water bodies.
Generally, higher slopes lead to higher resistance values and thus to higher
construction costs, as railway tracks are restricted to a maximum gradient of
3.5 % on a distance of 6 km. For higher slopes, the terrain has to be modified
by embankments, for instance, or either tunnels or bridges have to be built.
This increase of costs is approximated by higher resistance values for certain
raster points on the map. Tunnels and bridges are assumed to be about
7 times more expensive than the basic cost value.
Concerning the population map, a higher population density leads to higher
construction costs. In regions with a high density there are few open areas for
additional infrastructure available. We assume that for less than 100 persons
per raster pixel there is no increase of prices. For higher densities, we
calculated a linear growth.
Rivers, lakes and seas are further obstacles. Compared to the basic costs, the
crossing of rivers is related to a resistance factor of 5.4. Other water bodies
cannot be crossed except from the British channel. There the channel tunnel
has been implemented into the model and provides routes to Great Britain
assuming a resistance value of 1.5 due to the high user charges.
6.2 Results for European TOP 25 routes
Figure 7 depicts the course of the railway routes for the TOP 25 connections
in Europe as listed on the left side of table 1. As the channel tunnel is
implemented in the model, all routes can be technically realised. Track
construction in densely populated areas increases costs. Thus, the most cost
efficient routes take course in certain distance to city areas.
© Association for European Transport and contributors 2010
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The links of single lines are not considered by the model. The routes north
from Paris, for instance, are almost parallel. In reality, the connection from
Paris to London or Brussels leads via Lille. In fact, there is a bypass for Lille,
but a completely separate track is not reasonable in terms of operation and
construction costs. Moreover, passenger volumes of single point-to-point
connections add up. Thus, utilised capacity of routes and trains can be
optimised as long as travel time losses due to additional stops and detours
are low.
Figure 7:
7
Cost effective paths of highly frequented routes in
Europe
Conclusions and Outlook
The presented results give an overview of promising HSR routes in Europe
and identify potentials in Brazil, China, India, and the USA. In order to improve
the quality of the results both for passenger volumes and operating costs,
there are several aspects that can be addressed next.
We developed a gravity model that predicts annual passenger numbers on rail
routes by analysing the relationship between travel time, the GDP of cities and
their number of inhabitants. The model which has been calibrated on German
rail data provides a high statistical quality. A comparison with estimations for
passenger volumes on the transeuropean network (TEN) shows that the
results of the gravity model coincides with those of the TEN [9]. The model
allows evaluating the effects of changes in GDP, population or average speed
on the passenger volume of rail routes.
As for the NGT vehicle, an increase of average speed is in focus, we
calculated passenger volumes for various speeds. The model predicts a
disproportionately high growth of passenger volumes when average speed is
increased. This indicates that decreasing travel time by accelerating average
© Association for European Transport and contributors 2010
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speed leads to significantly better utilised capacities on HSR routes. For
higher speed than investigated, the linear correlation assumed in the gravity
model is not applicable as it does not evaluate the saturation of traffic
volumes.
The model will be further modified. Due to the calibration on German data, the
model underestimates passenger volumes on larger distances. Thus, the
connection Barcelona – Madrid does not appear in the European TOP 25
routes while it is one of the most frequented connections in Europe.
Therefore, in a next step, the gravity model will be calibrated using data on
various existing European railway lines. Furthermore, some city connections
have individual characteristics and require further investigation. Routes to
Brussels, for instance, are supposed to be highly frequented due to the city’s
political importance in the EU.
For analysing regions outside Europe, we estimated passenger volumes
based on seat capacities for air traffic. As for the investigated connections
HSR is supposed to substitute air traffic, this assumption indicates possible
traffic volumes for high speed transport. The modal shift from air to rail is
estimated according to experiences from existing HSR lines around the world.
Additionally, the applicability of the gravity model in regions outside Europe
should be analysed. By improving the data quality on passenger volumes,
operation concepts for the NGT can be elaborated for possible HSR networks
in the investigated countries. This will lead to better estimations of operation
costs.
For the assessment of potential HSR routes, operating costs are an important
factor. We evaluated those costs by estimating required numbers of vehicles
for each route and travelled distances additionally to the results of the
passenger volume models. As passenger volumes and thus operating costs
and profitability of routes strongly depend on travel time, future HSR lines
should provide higher average speed as today. One possibility of increasing
the average speed is to increase operating speed of trains. When combined
with relatively low energy consumption as in case of the NGT, the resulting
passenger volumes, travel time and operating costs are promising in view of
competition to air traffic.
Furthermore, the proposed analysis of possible HSR routes and networks will
indicate potential energy and emissions savings through substitution of air
traffic by HSR.
In order to enable the reduction of travel time on city routes, an appropriate
HSR infrastructure has to be provided. For a more detailed analysis of
construction costs, it is useful for to investigate additional information like
soils, geology or protected areas. Furthermore, resistance coefficients can be
adjusted to route specific parameters that are different for each country for
example. We will further improve the model by analysing required tunnels and
bridges on the predicted routes using data on the elevation profiles.
For the planning of a HSR route in a region, it is necessary to consider
possible networks rather than evaluating point-to-point connections. In an
optimal network, traffic volumes of single connections add up when
intermediate stops are inserted or city links are expanded to include a third
city. Yet the building of networks usually contradicts an increase of the
average speed as additional stops and detours lead to increased travel times.
Thus, both for infrastructure planning and operating costs, the examination of
© Association for European Transport and contributors 2010
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possible HSR networks will lead to more detailed results for potential HSR
lines in the countries investigated in this paper.
These results will lead to further quantification of the benefits from the new
NGT vehicle concept. By improving the attractiveness of HSR, a significant
amount of flights and thus greenhouse gas emissions can be substituted.
8
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