1. No category

Entry and competition in freight transport:
How to best get your potatoes from Lyon to Turin∗
Delphine Prady, University of Toulouse I
Hannes Ullrich†, Centre for European Economic Research (ZEW), Mannheim
work in progress, September 2006
Abstract
We analyze the expected effects of introducing a new product on the market shares of the established and the new suppliers, and on consumer
surplus. In particular, we apply the equilibrium model presented by Ivaldi
and Vibes (2005) to the alpine freight transport market. The object of our
analysis is the planned introduction of a 55km rail tunnel creating a new
link between Lyon and Turin. In our framework freight shippers choose
a mode and an alpine path to send goods from a given origin to a given
destination, while freight carriers strategically set prices for the differentiated products they supply. Deriving the market equilibrium we are able to
simulate the entry of this new and quality–improved product and test its
competitive viability within the market. Based on our collected data and
several simulations, we show the competitive weakness of the prospective
Italian–French alpine path which is far from yielding the modal shift that
is hoped for.
∗
We thank Marc Ivaldi for bringing this subject to our attention. We are particularly
thankful for invaluable comments given by Catherine Vibes, Moritz Kuhn, Bertrand Pell´e and
Georg B¨
uhler and to Thomas Spiegel of the Austrian BMVIT for providing the CAFT data.
†
ZEW, L7,1, 68161 Mannheim, Germany, E-Mail: [email protected]
1
Introduction
Public infrastructure investment begins to receive the attention in economic
analysis it deserves. For very long, decisions to invest in large transport infrastructure have been determined to a non-negligible extent by political pressure
stemming from electoral concerns and political influence activities. Cadot et al.
(2004) find little empirical evidence for economic maximization rationale with
respect to productivity growth but strong evidence for purely political motivation. Thus, one way to analyze the economic returns of public investment
is to find and measure the causal relationship from investment to productivity growth. An alternative approach is presented in this paper, that is the
application of an equilibrium model in order to calculate the changes in consumer surplus for direct infrastructure users and in external costs for all agents
affected by the new infrastructure. This latter approach allows to explicitly
take into account the competitive nature of the use of the concerned transport
infrastructure.
More particularly, we analyze the competitive dimension of freight transport supply, in particular the competition among rail and road freight carriers
on the Lyon–Turin corridor. We perform an equilibrium analysis in the context
of a discrete choice model (see Anderson et al. (1992) or McFadden (1981))
which allows us to analyze the competitive supply of differentiated products.
Indeed, we can predict the reactions of all competitors, following their equilibrium strategies as well as consumer behavior when facing a new product1 .
In this framework, we simulate the entry of a planned high speed transalpine
rail link, ‘La Liaison Ferroviaire Lyon–Turin’, on two distinguished markets:
regional freight traffic between Lyon and Turin, and transit freight traffic between Spain and Italy. Other structural changes such as the entry of a low cost
competitor will also be considered in the following. Heterogeneity of freight
transport as a commodity2 as well as a statistical analysis of the Alpine freight
transport on an Origin–Destination (O–D) basis led us to spatially distinguish
between the two markets mentioned above. Indeed one might expect fiercer
inter–modal competition in a long distance freight transport market where rail
products are said to be more competitive relative to road. Intra–modal competition, however, should play a more important role in the short distance market
with differentiated transport products. Products considered hereafter consist
of a transport mode — rail or road — and a cross–alpine path3 . Their differ1
I.e. a new rail link.
Specific haulage requirements, logistic needs, and conveyed goods.
3
Mont Blanc, Fr´ejus, Vintimille, Montgen`evre, Mont–Cenis.
2
2
entiation is mainly due to geographical and regulatory aspects. For example,
length, altitude or type of allowed trucks/wagons vary from one cross–alpine
tunnel or pass to another. We claim that shippers have in mind these qualitative differences of available products when sending their goods on a given O–D.
Hence, here, the setting of competition with differentiated products is very well
applicable.
The paper is organized as follows. In section 2 we describe our demand–
and–supply model. Our data set on the supply as well as the demand side is
presented in section 3. Further assumptions we made on the data are explained
in this section. In section 4, we provide our empirical analysis and simulation
results which are followed by a cost-benefit perspective in section 5. Section
6 concludes. For a more general overview, in appendix B we describe major
trends in European freight transport and emphasize the new characteristics of
the ‘Liaison Ferroviaire Lyon–Turin’ project.
2
Modeling Lyon–Turin Freight Transport
Our goal is to evaluate the implied changes on social welfare of a rail infrastructure project on a specific O–D. In our setting, based on Ivaldi and Vibes
(2005), consumers, which here are transportable goods producers and hereafter
called shippers, choose a transport mode (rail / road) and an alpine path to
carry their goods between two specific regions. Cross–alpine goods transporters,
hereafter called freight carriers, are assumed to compete either in prices or in
quantities offering consumers a specific product ‘mode and path’ on the O–D.
We then derive the market equilibrium and provide results of counterfactual
experiments.
Note that freight services are not homogenous goods but consist of a widely
diversified set of goods with specific haulage requirements and logistic needs.
Furthermore, as a spatial concern, one needs to distinguish between transit and
regional freight transport4 . Our model will therefore be highly stylized and
simplified.
As noted above, on the demand side, we consider two types of ‘potential
markets’ targeted by cross–alpine freight carriers: transit transport and regional
transport. Based on its high share on French–Italian transalpine passages5 we
4
Transit and inter–regional freight carriers inhibit different company characteristics, e.g.
long–haul transit freight carriers are rather firms with > 50 employees, inter–regional mid–haul
freight carriers are rather smaller firms (See, for example, London Economics (2003).
5
See Figure 9.
3
assume freight carriers in transit to move between Spain and Italy. Furthermore,
we analyse inter–regional transport moving between the area of Lyon and the
area of Turin. The heterogeneity of commodities is taken into account as we
borrow from Oum et al. (1990) the own price elasticities of rail/road transport
corresponding to the carried commodities through the Alps. In addition to the
competing differentiated ‘mode + path’ products, we assume the existence of
an outside good, OG. This OG accounts for shippers that would be interested in
transporting their goods across the Alps but that actually do not: it represents a
potential niche cross–alpine freight carriers can target, thereby inducing further
traffic. This may become an important observation since total consumer welfare
will increase due to additional induced traffic.
On the supply side, cross–alpine freight carriers6 offer shippers to transport
their goods from one origin point to one destination point through a given
alpine tunnel. We assume that they are profit maximizers. Furthermore, they
are assumed to compete either in prices or in quantities.
2.1
Demand side
Assume each shipper makes her choice in two logical steps:
• first, she decides which mode she wants to carry her commodity with,
• second, she chooses an alpine path7 .
An alternative or a product is thus a combination of a transport mode and a
path to cross the Alps. There are J alternatives classified into G groups. We
have three groups: g = 0, 1, 2, where the group 0 corresponds to the outside
alternative (OG) and the two other groups correspond to the considered modes:
rail and road. The utility function associated with alternative j is as follows:
Uij = Vj + ij
(1)
where:
• Vj is the mean utility level common to all shippers,
6
E.g. SNCF and Trenitalia for the Lyon–Turin rail project.
This assumption might sound rather unrealistic. However, different paths and modes
are characterized e.g. by different speeds and reliabilities, therefore representing qualitatively
differentiated goods.
7
4
• ij corresponds to the departure of the shipper i from the common utility
level (also called random part, i.e. unknown shipper i’s taste for product
j). The random component leading to a “nested logit” demand model is
specified as follows:
ij = σ νig + (1 − σ) νij
(2)
with σ being the degree of correlation between alternatives j belonging to the
same group g; a high σ means the shippers give a higher weight to the group
than to the alternative itself in their choice. Thus, competition is fiercer between modes than between alpine paths. To be in line with the random utility
maximization concept this parameter σ must lie between 0 and 1. In the extreme case of symmetric competition where the assumption of independence
of irrelevant alternatives (IIA)8 holds between all alternatives, σ will take on
the value of 0 and the model reduces to the simple logit specification. At the
other extreme, in the segmentation case, where preferences for alternatives are
perfectly correlated within nests but independent between nests, σ will be equal
to 1. Random components νig and νij are assumed to be distributed such that
each term, and consequently also ij , is standard extreme value distributed.
We assume the mean utility level to be:
Vj = Ψj − h pj
(3)
where:
• Ψj is the aggregate measure of quality of product j and
• h represents the sensitivity of utility to price, i.e. the marginal utility of
cost saving for the shipper.
We then compute the aggregate measure of quality as the weighted sum of the
characteristics of alternatives:
Ψj = α1 altj + α2 accj + α3 traveltimej + α4 f requj + d Dj
where:
• altj = the altitude of the path j, expected negative sign for α1
8
See McFadden (1981)
5
(4)
• accj = number of accidents per km and per year (between 1998 and
2000)9 , expected negative sign for α2
• traveltimej = reliability measure of the different alternatives, expected
negative sign for α3
• f requj =
1
F requency ,
with expected negative sign for α4 .
• Dj = dummy variable (Dj = 1 if road) taking into account e.g. the fact
that the mode ‘road’ is more flexible relative to the mode ‘rail’
We assume these characteristic variables to be the most relevant for shippers
to account for quality in alpine tunnels.
Shipper i chooses the utility–maximizing alternative j, satisfying:
Uij ≥ Uik
∀ k 6= j
(5)
Normalizing the mean utility of the outside good to zero, we can compute the
probability of choosing alternative j from the probability of choosing group g
and the probability of choosing alternative j conditional on choosing group g.
We apply the methodology developed by Berry (1994), which is based on the
assumption that observed aggregate market shares are valid approximations of
choice probabilities. This methodology allows us to derive the mean utility
levels as follows:
ln sj − ln s0 = Ψj − h pj + σ ln sj/g
(6)
with sj and sj/g respectively being the total market share and the group market
share of alternative j.
Finally, the own price elasticity of demand of the alternative j is:
µj = h pj sj −
2.2
1
σ
+
s
1 − σ 1 − σ j/g
∀ j∈g
(7)
Supply side
We focus on the competitive aspect of cross–alpine freight transport. Competing freight carriers offer shippers a differentiated product combining a transport
mode — rail or road — with a specific alpine tunnel or pass — MB, FRE, MG,
VIN or MC10 (see figure 1). We therefore consider five distinguished firms, each
9
http://www.securiteroutiere.equipement.gouv.fr/vos-infos/departement/accidentalite.html
MB = Mont Blanc, FRE = Fr´ejus, MG = Montgen`evre, VIN = Vintimille, MC = Mont–
Cenis.
10
6
Figure 1: Basic discrete choice model for regional traffic between Lyon and
Turin
The shares of the GSB and SG paths in regional traffic between Lyon and Turin are so
negligible (respectively 0.09 % and 0.04 % of their group market) so that we will not
consider them as relevant competitive products for the inter–regional Lyon–Turin traffic.
offering one product. Note that the product ‘road + specific tunnel passage’
is assumed to be offered by four firms which is quite a strong simplification of
reality – where the road freight industry inhibits a rather atomistic structure.
Indeed, in France in 2000, 77.6% of road freight carriers maintained 0 to 5 employees, 2.3% of all transport companies had more than 50 employees. In terms
of revenue, freight carriers with less than 50 employees accounted for 59.4% of
the industry’s total revenue11 .
We propose two equilibrium specifications — one in which carriers compete
in prices and one where they compete in quantities.
In these two scenarios, cross–alpine freight carriers respectively set transport
prices/quantities in order to maximize their profits knowing their competitors
do the same:
M ax Πj = (pj − cj ) qj − K
(8)
with fixed costs K.
Competition `
a la Bertrand
If these five firms compete in prices the outcome is defined by the well–known
11
Source: Page 4 in EUROSTAF (2003).
7
set of J necessary first order conditions, e.g. from Ivaldi and Verboven (2005):
pj = cj +
1−σ
h 1 − σ sj/g − (1 − σ) sj
(9)
Competition `
a la Cournot
If freight carriers instead set quantities transported as their strategic variables
prices are (See Ivaldi and McCullough (2005):
1
pj = cj +
h
sj/g sj
1+
− σ 1 − sj/g
sj/g − sj
(10)
The price of product j is thus its marginal cost, cj , and a mark–up term that
differs subject to the specification of the competitive structure on the supply
side12 .
3
Data
3.1
Supply side
In order to characterize the available path alternatives and find approximations
to freight carriers’ marginal costs, data on costs and prices of infrastructure
use, fuel consumption, personnel are collected. We collected data pertinent
to a typical transport unit of 20 tons. The description of our obtained cost
values follows below. Note that the mentioned components represent short–run
variable costs which we use to approximate marginal costs. In our case this
seems to be reasonable and, indeed, our cost values are rather close to cost
values per ton per km (tkm) presented in Herry (2001).
Marginal costs – Road
For trucks, marginal costs include costs of infrastructure use, such as road
and tunnel fees, fuel costs, and costs of personnel. The former are available
from infrastructure operators, i.e. highway and tunnel operators. Since we do
not have exact data on fuel consumption on each path we use an average fuel
consumption of 0.355 liters per truck per km on regular routes and 0.6 liters
per truck per km on mountainous routes and from this calculate the different
12
In the current version of the paper we only report the empirical results for Bertrand
competition. The results for Cournot competition and a discussion of assuming Bertrand or
Cournot will follow.
8
fuel consumption values using the distance on each path13 . Using the per liter
price for truck diesel in January 2000 of 0.62 Euro cents we can calculate the
costs for fuel on each passage. For personnel costs we use the value of 20 Euros
per hour14 .
Prices – Road
Pricing in truck freight is mainly done according to the type of goods transported, weight and distance. Obviously, these components permit much room
for price discrimination that we cannot take into account in this study. A more
realistic stance would be taken in implying the specificities of price setting behavior in the road freight sector, e.g. non–linear pricing. Here, we use prices
generated by a pricing tool used by a typical road freight carrier. For more
precise results, a survey on prices in road freight should be conducted.
Marginal Costs – Rail
In rail transport, marginal costs are given by the costs of infrastructure use,
fuel consumption, and costs of personnel. Data on infrastructure charges can
be found either at the RFF that manages and operates the French rail network
or for all Europe at the EICIS Portal15 . Energy consumption of a standard
locomotive pulling a standard train of 800 tons16 should be considered here
while taking into account the higher energy consumption on tracks that inhibit
steeper slopes. As we were not able to extract values on operational costs of
freight trains from several interviews with large rail freight companies we have to
use rather hypothetical values here. Again, knowing exact marginal cost values
could enhance the quality of our results. Furthermore, note that there obviously
exists a remarkable degree of heterogeneity in train technologies, train sizes and
weights that we must also leave aside in this study for the sake of simplicity.
Prices – Rail
For rail prices, we take tariffs for a 20t shipment on a standard 4–axle train
wagon with a capacity of 60t on the distance of the existing rail link from the
freight tariff scheme of the SNCF17 . From an interview with a representative of
a large European freight carrier we know, however, that actual prices usually
13
Fuel consumption values are geared to values from Girault et al. (2000) and Grotrian et
al. (2001).
14
We obtained this information from a personal interview.
15
http:// www.eicis.com
16
See also Christen et al. (2004).
17
http://fret.sncf.com/fr/espclnt/ncc/index.asp
9
lie about 15% below these tariffs, due to the possibility of negotiation, quantity
discounts and else. For more precise data on this, again, a more elaborate
survey on prices would be needed.
Data Values for the different alternatives on the inter–regional market
Table 1 shows prices and marginal costs for each passage and mode. Table
2 presents the above–described quality components of the 5 existing alternatives as well as the new transalpine link. Frequencies of train departures are
taken from LTF (2003), accident values are published by the French Ministry
of Transportation18 and travel times in hours are calculated based on speed,
distance and stopping periods.
Table 1: Prices and Marginal Costs for passages from Lyon to Turin per 20t
load (In Euros)
Road
Road
Road
Road
Rail
Mont–Blanc
Fr´ejus
Montgen`evre
Vintimille
Mont–Cenis
MC
500.2
463.1
316.7
521.0
500.0
Price
850
771
540
1213
720
Table 2: Characteristics of the different alternatives for freight transport between Lyon and Turin
Road
Road
Road
Road
Rail
Rail
Mont–Blanc
Fr´ejus
Montgen`evre
Vintimille
Mont–Cenis
New transalpine link
Altitude
1328
1158
1860
9
1158
478
Accidents
0.2
0.2
0
0.25
0
0
Travel Time
7.02
5.73
8.16
12.36
11.60
6.00
Frequency
Infinity
Infinity
Infinity
Infinity
45
80
Data Values for the different alternatives on the transit market (Spain
<—> Italy)
18
http://www.securiteroutiere.equipement.gouv.fr/vos-infos/departement/accidentalite.html
10
For values on the transit market we consider what we call ‘geographic averages’, i.e. we simply use price and cost values for the O–D relationship Madrid
- Bologna. This is admittedly quite a strong simplification but should, on the
average, still produce useful results. A much more complex analysis would be
needed if all details of intra–European transit were to be fully taken into account. Table 3 shows prices and marginal costs for each passage and mode in
Spain–Italy transit.
Table 3: Prices and Marginal Costs for passages from Spain to Italy per 20t
load (In Euros)
Road
Road
Road
Road
Rail
Rail
Mont–Blanc
Fr´ejus
Montgen`evre
Vintimille
Mont–Cenis
Vintimille
MC
1433
1475
1221
1125
1000
1200
Price
2825
2864
2537
2383
2246
2316
Table 4 presents the quality components of the 6 existing alternatives as
well as the new transalpine link. All data were obtained as above except of
the frequency on the rail passage through Vintimille which is a hypothetical
value, based upon its market share and the existing infrastructure along the
Mediterranean coastal line.
Table 4: Characteristics of the different alternatives for freight transport between Spain and Italy
Road
Road
Road
Road
Rail
Rail
Rail
Mont–Blanc
Fr´ejus
Montgen`evre
Vintimille
Mont–Cenis
Vintimille
New transalpine link
Altitude
1328
1158
1860
9
1158
9
478
11
Accidents
0.2
0.2
0
0.25
0
0
0
Travel Time
28,73
28,17
30,31
25,38
42,98
28.86
37
Frequency
Infinity
Infinity
Infinity
Infinity
45
20
80
3.2
Demand side
For the random utility framework we aim to work with to analyze the benefits
from the planned Lyon–Turin link, individual level data is needed in order to
model decisions based on observable and unobservable product and consumer
characteristics. This kind of micro–level data can be produced through expensive and time–consuming surveys that are not feasible in the scope of this paper.
Thus, since we do not dispose of appropriate micro–level data, we chose to use
the inversion method proposed by Berry (1994), which uses aggregated data
such as market shares and information on prices and some quality variables
within the discrete choice framework. Applying this method (along the line
with Ivaldi and Vibes (2005)) on the O–D pair Lyon–Turin requires finding
market shares of passages on this particular link. Therefore, we define two
markets:
• goods producers and freight carriers in the regions around each Lyon and
Turin;
• goods producers and freight carriers in Spain and Italy,
transporting their goods between these two regions and countries, respectively.
In Appendix B.2 the geographical markets considered are defined in some more
detail.
Table 5: Inter–regional freight traffic shares of French–Italian passages between
Lyon and Turin, 1999 – With outside option share of 15%
Passage
Road
Road
Road
Road
Rail
Mont–Blanc
Fr´ejus
Montgen`evre
Vintimille
Mont–Cenis
Market Share
5.34 %
74.73 %
4.30 %
0.58 %
0.05 %
We obtain the market shares (based on tons transported) of each alpine
passage from the CAFT19 databases 1994 and 199920 which collect information
on freight transport with regard to origin, destination, alpine passage, transport
19
‘Cross–Alpine Freight Transport’, Collected by Austrian, French and Swiss authorities
over the entire alpine arch.
20
Unfortunately, the CAFT survey is only done every 5 years and the most recent survey
data that has been collected in 2004 is not yet available due to the lengthy data harmonization
process.
12
mode, weight, etc. Table 5 illustrates these shares for the inter-regional traffic
between Lyon and Turin which accounted for a total of approximately 2 Mio
tons in 1999. In relative terms, this amounts to 4% of all traffic crossing the
French-Italian Alpine corridor.
A particular problem in using the database from 1999 arises due to the
severe accident that occurred in the Mont–Blanc tunnel end of March 1999.
The tunnel has been closed for general traffic until March 2002 and for freight
traffic until June 2002. Therefore, Mont–Blanc tunnel traffic had to switch to
other near–by passages. Basically all of this traffic was compensated by the
Fr´ejus tunnel. Since we have access to the 1994 database as well, we infer
from the 1994 passages’ shares reasonable assumptions on the passages’ shares
in 1999 if the Mont-Blanc tunnel had not been closed. We cannot account,
however, for overall lost traffic caused by the tunnel closure. This prevents us
from deleting the characteristics of an alternative Lyon—Turin path through
the Mont—Blanc tunnel21 .
Table 6: Transit freight traffic shares of French-Italian passages between Spain
and Italy, 1999 - With outside option share of 15%
Passage
Road
Road
Road
Road
Rail
Rail
Mont–Blanc
Fr´ejus
Montgen`evre
Vintimille
Mont–Cenis
Vintimille
Market Share
0.07 %
0.71 %
5.10 %
79.11 %
0.0022 %
0.0013 %
Table 6 presents the passage shares for transit traffic between Spain and
Italy which accounts for a total of 6.7 Mio tons in 1999. Again, in relative
terms, this amounts to just above 13% of all traffic crossing the French-Italian
Alpine corridor. On this O–D relation the road passage through Vintimille
plays the dominant role. However, the train passage through the Mont–Cenis
captures a larger part than its counterpart in Vintimille, namely 0.0022% versus
0.0013%. Therefore, for the mode train, a new and better performing link may
be able to build up some market share.
21
Among others, this problem should be solved as soon as the latest CAFT survey data
from 2004 becomes available and the analysis can thus be re-run on better data.
13
4
Empirical Analysis and Results
Shippers’ and freight carriers’ actions depend on their sensitivities to changes
in the alternatives’ characteristics. The prospective rail link between Lyon and
Turin will bring such changes whose impact on the equilibrium market shares
and prices as well as shippers’ surplus we want to measure. However, before
rushing into this simulation analysis, we need to derive the equilibrium features
of our market including only the presently available alternatives shippers may
chose from. As a complete set of data including elasticity and marginal cost
values as well as demand parameters is not readily available, a straight statistical estimation of our model is impossible. Therefore, we need to calibrate the
model, i.e. find the equilibrium values of the demand parameters, h and σ as
well as the quality parameters in Equation (4). Once this is done, we are able
to define the equilibrium outcome and do the simulation on the change of this
outcome if the new link is introduced. All numerical calculations were done
using M atlab.
4.1
Model Calibration
Following the procedure by Ivaldi and Vibes (2005) we first derive the demand
parameters h and σ. To do so, we invert Equation (7) defining price elasticities. Note we do not have data on elasticities but we do on marginal costs. We
thus repeatedly generate vectors of elasticities, based on the normal distribution
function. We derive the mean of this distribution from the commodity–specific
values presented in Oum et al. (1990), weighted by the commodity shares transported on each link; considering the rather extreme market shares22 we observe
in this particular model, we have to make additional assumptions for concerned
links. We then associate a value of h and σ to each draw of elasticities’ vector, allowing us to derive a marginal cost vector from Equation (10) for each
of these draws. We finally keep the h and σ values, as well as the associated
vector of elasticities µj , rendering the marginal cost vector that is closest to
our data. Now, we use these results to proceed to resolve the system of equations defining the quality parameters Ψj , the quality components described in
Sections 2.1 and 3.1, and their coefficients in Equation (4). We thus solve a
system of five linear equations, allowing us to derive consumers’ valuations for
each quality variable. Our results on elasticity and parameter values, i.e. the
current equilibrium outcomes are presented in Table 7.
22
For these, the standard elasticity values from the literature do not make much sense.
14
15
Share of outside alternative in %
Mont–Blanc
Fr´ejus
Road Market Shares in %
Montgen`evre
Vintimille
Mont–Cenis
Rail Market Shares in %
Vintimille
Marginal utility of costs saving (Parameter h)
Degree of within–group correlation (Parameter σ)
Mont–Blanc
Fr´ejus
Own–Price Elasticities — Road
Montgen`evre
Vintimille
Mont–Cenis
Own–Price Elasticities — Rail
Vintimille
Consumer Surplus
Inter–Regional (Lyon–Turin)
15
30
45
5.34
4.40
3.46
74.73
61.54
48.35
4.30
3.54
2.78
0.58
0.48
0.38
0.05
0.04
0.03
0.0021 0.0023
0.0020
0.75
0.71
0.69
- 6.75 - 6.38
- 5.22
- 1.00 - 1.20
- 1.23
- 4.34 - 4.10
- 3.36
- 10.19 - 9.60
- 7.83
- 1.52 - 1.64
- 1.47
831.3
373.0
98.3
Table 7: Equilibrium outcomes
Transit (Spain–Italy)
15
30
45
0.07
0.06
0.05
0.71
0.59
0.46
5.10
4.20
3.30
79.11
65.15
51.19
0.0022
0.002
0.001
0.0013
0.0009
0.0007
0.00046 0.00044 0.00035
0.82
0.83
0.86
- 7.18
- 7.22
- 7.33
- 7.22
- 7.27
- 7.38
- 6.07
- 6.12
- 6.21
- 0.57
- 0.71
- 0.78
- 2.85
- 2.83
- 2.74
- 3.98
- 3.98
- 3.96
3768.3
1945.1
567.0
Intuitively, the rather small value of h can be explained by saying that
individuals will have a larger marginal utility of income than a firm’s marginal
utility of saving costs. In the transit market, it is even lower which may be
explained by the fact that long–distance freight shippers may be assumed to be
rather large companies while short– and medium–distance freight shippers are
more likely to be relatively smaller companies caring more about cost savings.
The high value of σ underlines the low substitutability between the nests of
differentiated products in the alpine freight transport market. The mode choice
remains the main component of the shippers’ decision.
In terms of market shares, both markets exhibit a dominant alternative
which does not vary with the OG market share: Fr´ejus in the inter-regional
market, Vintimille (road) in the transit market. This demand rigidity is also
reflected in the price–elasticities ranking, the lowest price–elasticities being associated with the Fr´ejus and Vintimille (road) alternatives. Values of the quality
indices and the weights of the quality indices components are presented in Table
8.
16
17
Dummy ‘Road’
1
F requency
Share of outside alternative in %
Mont–Blanc
Fr´ejus
Quality Index — Road
Montgen`evre
Vintimille
Mont–Cenis
Quality Index — Rail
Vintimille
Quality Index — New Transalpine Link
Altitude
Accident
Quality component coefficients Travel time
Inter–Regional (Lyon–Turin)
15
30
45
2.82
1.99
1.07
3.31
2.56
1.74
2.12
1.22
0.37
3.04
2.19
1.13
- 4.20
- 4.98
- 5.84
- 2.05
- 2.44
- 2.86
- 0.0011 - 0.0013 - 0.0014
- 0.80
- 1.19
- 2.20
- 0.23
- 0.27
- 0.33
- 10.64
- 13.88
- 17.10
6.09
5.87
5.72
Transit (Spain–Italy)
15
30
45
1.76
0.88
0.24
2.19
1.28
0.56
2.39
1.47
0.72
2.82
1.87
1.03
- 7.38
- 8.45
- 9.31
- 7.51
- 8.25
- 9.42
- 5.44
- 6.44
- 7.22
- 0.0007 - 0.0005 - 0.0003
- 5.22
- 4.97
- 4.05
- 0.11
- 0.14
- 0.16
- 86.97
- 84.85
- 96.87
6.92
6.65
6.08
Table 8: Quality Indices and quality component weights for all alternatives
Quality indices indicate the shippers’ large preference for road alternatives,
in both markets and for any OG share. This result is mainly due to the high
value of the road dummy coefficient. As said before, this dummy can be interpreted as a mode flexibility index, which obviously favors road alternatives
over rail ones23 . Its coefficient value is so high that, even though of greater
‘observable’ quality, the new transalpine rail link ranks worse than the least
preferred road option – Montgen`evre in the inter–regional market, Mont Blanc
in the transit market. This is also consistent with our σ value: variables used for
product differentiation enter shippers’ decisions in a less significant way than the
mode choice, captured by the road dummy. In this respect this dummy variable
certainly captures other quality criteria unobservable to the econometrician but
relevant in shippers’ choice. Note that all quality component coefficients have
the expected sign so that related variables have the expected effect described
above.
As we do not know the exact market configuration, the OG share is assumed
to vary between 15 and 45% of the total freight transport market. Alpine
traffic forecasts could help to determine the situation we are in. However, these
forecasts differ across scenarios and do not appear reliable. Indeed, most of
them tend to largely overestimate freight alpine traffic by the years 2020 and
203024 . In what follows we focus on the “OG = 15%” case for our equilibrium
and simulation analysis.
4.2
Simulations and Results
We can now simulate the entry of the new rail link using its quality characteristics and the demand parameters from the calibrated model. Appendix A
elaborates this procedure in more detail.
Entry of the new Lyon–Turin Rail link in the inter–regional market
Table 9 shows the results, again for different initial market shares of the
outside alternative, in point–to–point transport between Lyon and Turin.
23
24
Indeed, choosing rail instantly excludes change of mode on the whole trip.
See ECMT Report (2001).
18
19
Initial share of outside alternative in %
Values (in Euros) and Change in %
Mont–Blanc
Fr´ejus
Road prices
Montgen`evre
Vintimille
Mont–Cenis
Rail prices
New Transalpine Link
Mont–Blanc
Fr´ejus
Road market shares in %
Montgen`evre
Vintimille
Mont–Cenis
Rail market shares in %
New Transalpine Link
Market share of outside alternative in %
Consumer surplus
Value
639.78
722.51
441.01
680.09
618.64
975.58
13.30
47.53
4.20
22.39
0.0007
0.21
12.39
937.82
Inter–Regional (Lyon–Turin)
15
30
45
∆ Value
∆ Value
∆
- 24.7 649.24
- 23.6 684.01
- 19.5
- 6.3 743.68
- 3.5 818.44
+ 6.2
- 18.4 449.32
- 16.8 481.31
- 10.9
- 43.9 685.47
- 43.5 706.25
- 41.8
- 14.1 625.78
- 13.1 654.00
- 9.2
- 939.95
- 991.45
+ 149.0
11.60
+ 163.6
9.37
+ 170.8
- 36.4
40.79
- 33.7
32.67
- 32.4
- 2.3
3.89
+ 9.9
3.74
+ 34.5
+ 3760.3
17.44 + 3533.3
9.75 + 2462.5
- 98.5 0.0004
-98.9
0.02
- 33.3
0.27
0.33
- 17.4
26.02
- 13.3
44.14
- 1.9
+ 12.8
459.8
+ 23.3 115.14
+ 17.1
Table 9: Equilibrium outcomes after introduction of the new transalpine rail link
The new alternative is assumed to have the same marginal cost as the historical Mont–Cenis alternative served by SNCF.
A global overview of this first simulation result brings to light three important effects of the new link provision in the regional market:
• all prices decrease,
• global rail market share substantially increases,
• consumer surplus increases.
Going into the details of these general observations we can see that prices do not
decrease homogenously. The 6.3% decrease in the price of alternative Fr´ejus is
far from the 18.4%, 24.7%, and even 43.9% decreases in the Montgen`evre, Mont–
Blanc and Vintimille alternatives’ prices respectively. The rather captive Fr´ejus
demand25 in terms relative to the other alternatives’ more volatile demands
explains this large difference in the road freight carriers’ pricing reactions. Thus,
freight carriers’ pricing behavior seems to be highly sensitive to inter-modal
competition. All road carriers decrease — tremendously in the case of the
Vintimille alternative — their prices in order to maintain their market shares.
Compared to the supply side, demand reactions seem more sensitive to
intra–modal competition. Indeed, the post–entry market shares’ reallocation
occurs within mode nests and not across them. The substantial increase in the
rail market share is largely due to the increase of global market size26 and not
to modal shift. Within the road nest we observe a significant decrease of the
Fr´ejus market share, quasi–entirely in favor of the Vintimille alternative. As
for rail alternatives, their total market share increases by 321.4% but remains
very low relative to the road alternatives’. If the new rail link seems more
competitive, it is at the expense of the historical rail alternative Mont–Cenis.
As a concluding remark, we see that the new ‘Liaison Ferroviaire Lyon–
Turin’ derives its demand from potential shippers and not from shippers already
consuming road products. The decrease in the OG’s market share induces an
improvement of the consumer surplus. However, this consumer gain remains of
rather low magnitude.
Entry of the new Lyon–Turin Rail link and an extra low–cost competitor on this new link, in the inter–regional market
25
26
See the own price elasticity µF rejus = −1.0.
See the OG share decrease of 17.4%.
20
In this simulation (see Table 10) we introduce a ‘low–cost’ rail freight carrier on the new high speed link between Lyon and Turin. This new carrier is
assumed to have only half of the labor costs of the rail incumbent27 . Then,
the marginal costs are 350 euros per 20 tons. Taking into account a potential
‘hubbing’ power of the SNCF28 we grant our low cost carrier of only 30% of the
slots allocated to the SNCF on the new link.
27
namely SNCF.
which has developed a long term relationship with the infrastructure operator RFF and
therefore still has a remarkable advantage in terms of infrastructure access.
28
21
22
Initial share of outside alternative in %
Values (in Euros) and Change in %
Mont–Blanc
Fr´ejus
Road prices
Montgen`evre
Vintimille
Mont–Cenis
Rail prices
New Transalpine Link
Low–cost competitor
Mont–Blanc
Fr´ejus
Road market shares in %
Montgen`evre
Vintimille
Mont–Cenis
Rail market shares in %
New Transalpine Link
Low–cost competitor
Market share of outside alternative in %
Consumer surplus
Value
639.78
722.51
441.01
680.09
618.35
664.46
575.01
13.26
47.39
4.18
22.32
0
0.19
0.32
12.35
939.26
Inter–Regional (Lyon–Turin)
15
30
45
∆ Value
∆ Value
∆
- 24.7 648.52
- 23.7 683.55
- 19.6
- 6.2 742.05
- 3.8 817.42
+ 6.0
- 18.3 448.69
- 16.9 480.88
- 11.0
- 43.9 685.07
- 43.5
706
- 41.8
- 14.1 625.11
- 13.2 653.57
- 9.2
- 693.67
- 744.85
- 546.19
- 574.34
+ 148.3
11.55
+ 162.5
9.35
+ 170.2
- 36.6
40.68
- 33.9
32.6
- 32.6
- 2.8
3.85
+ 8.8
3.72
+ 33.8
+ 3748.3
17.52 + 3550.0
9.76 + 2468.4
- 100
0
- 100
0
- 100
0.26
0.33
0.27
0.28
- 17.7
25.87
- 13.8
43.97
- 2.3
+ 13.0 461.25
+ 23.7 118.34
+ 20.4
Table 10: Equilibrium outcomes after introduction of the new transalpine rail link operated by both an incumbent and a low cost rail
carrier
Compared to the ‘entry of a high quality rail alternative supplied by the
rail incumbent only’ simulation, three remarks are of importance:
• consumer surplus increases by a larger amount,
• fiercer competition within the rail nest deters the incumbent to set its
prices too high,
• changes in prices and market shares within the road nest are quite similar
to those in the previous simulation.
Consumer surplus increases by 13%. Even if this is increase is slightly stronger
than the 12.8% in the previous simulation it remains of low magnitude. Again,
this benefit is due to induced traffic in the existing market: more potential shippers, gathered in the OG alternative whose market share decreases by 17.7%,
are attracted by the new infrastructure and its more attractive price. Indeed,
the low–cost entry embeds all alternatives’ prices in an average of 620 euros,
and rail alternatives’ prices in an average of 619 euros. Without the low–cost
alternative these averages were at 680 euros and 797 euros29 , respectively.
Fiercer competition among rail alternatives induces the incumbent to supply
its ‘high quality’ product at a much lower price than if he operated the new
link alone. Thus, its hubbing power allows it to set its price 15.6% above its
competitor’s. We are, however, far from the potential 70% difference if the
incumbent’s price had remained as high as in the no low–cost simulation. The
new alternative being less expensive and potential shippers being rather pricesensitive — see quasi–equal quality indexes: −2.05 ≈ −2.11 — the low–cost
alternative benefits from a larger market share than the incumbent.
Finally, the entry of two new alternatives in the rail nest seems to have
exactly the same effect on road carriers’ behavior as only one. Changes in
prices and market shares at the expense of the Fr´ejus alternative are rather
identical to those in the previous simulation. Moreover, road predominance on
the global freight market in the alpine region does not seem to be threatened
at all by comparably priced and technologically advanced rail supply.
We thus might wonder why rail apparently remains so uncompetitive relative
to road in the alpine region. Our specification of the mean utility value is
certainly too crude: a high part of it remains probably unobservable to us and
is not caught by our quality variables. Data on carried goods, specific transport
delays and deadlines as well as truck/wagon types allowed in cross–alpine paths
29
Prices are per loads of 20 tons.
23
would have been of great help in this respect. Unfortunately, such data were
not available to us.
Entry of the new Lyon–Turin Rail link in the transit market
(Spain <—> Italy)
Note that in the transit market between Spain and Italy shippers use, in
addition to our above inter–regional simulations, the rail path in Vintimille.
In Table 11 we see that while we expected fiercer inter–modal competition in
a long–distance market, results are really close to our previous regional ones.
The new rail link succeeds in attracting new shippers in the market. Once
again it benefits the most from the decrease by 7.5% in the OG share but fails
in capturing demand from road alternatives. Even if its higher quality makes
it the best rail alternative, in spite of a higher price, the new link does not
appear to be competitive relative to road supply. In this respect the ‘Liaison
Ferroviaire Lyon-Turin’ alone cannot be the relevant modal shift device its proponents claim it to be. Therefore, only a more global transport policy scheme
taking into the strategic behavior in both supply and demand may be capable
of achieving a substantial shift in transport modes.
Entry of the new Lyon-Turin Rail link and an extra low-cost competitor on this new link, in the transit market
Compared to the inter–regional market, the entry of a low cost alternative
(see Table 12) seems to cause what we may call a ’market breakdown’ due to
the too little market size. It does not seem to be able to permit the existence
of four alternatives next to each others.
24
25
Initial share of outside alternative in %
Values (in Euros) and Change in %
Mont–Blanc
Fr´ejus
Road prices
Montgen`evre
Vintimille
Mont–Cenis
Rail prices
Vintimille
New Transalpine Link
Mont–Blanc
Fr´ejus
Road market shares in %
Montgen`evre
Vintimille
Mont–Cenis
Rail market shares in %
Vintimille
New Transalpine Link
Market share of outside alternative in %
Consumer surplus
15
Value
∆
1828.9
- 35
1893.4
- 34
1755.7
- 31
2314.8
-3
1594.0
- 30
1393.9
- 40
3339.8
0.56 + 700
5.15 + 625
22.76 + 346
57.64 - 11.5
0
- 100
0
- 100
0.013
13.87
- 7.5
3966.5 + 5.3
Transit (Spain–Italy)
30
45
Value
∆ Value
∆
1826.4
- 35.3 1820.8
- 35.6
1890.7
- 34.0 1884.7
-34.2
1752.2
- 30.9 1743.9
- 31.3
2306.5
- 3.2 2287.1
- 4.0
1591.1
- 29.2 1585.3
- 29.4
1392.3
- 39.9 1385.3
- 40.2
3437.3
- 4021.8
0.47 + 683.3
0.37 + 640.0
4.30 + 628.8
3.41 + 641.3
19.00 + 352.4
15.02 + 355.2
48.08
- 26.2
38.15
-25.5
0
- 100
0
- 100
0
- 100
0
- 100
0.01
0.008
28.15
- 6.2
43.06
- 4.3
2150.6
+ 10.6 789.81
+ 39.3
Table 11: Equilibrium outcomes after introduction of the new transalpine rail link
26
Initial share of outside alternative in %
Values (in Euros) and Change in %
Mont–Blanc
Fr´ejus
Road prices
Montgen`evre
Vintimille
Mont–Cenis
Vintimille
Rail prices
New Transalpine Link
Low–cost competitor
Mont–Blanc
Fr´ejus
Road market shares in %
Montgen`evre
Vintimille
Mont–Cenis
Vintimille
Rail market shares in %
New Transalpine Link
Low–cost competitor
Market share of outside alternative in %
Consumer surplus
Value
1829.0
1893.4
1755.7
2314.8
1598.0
1397.2
3135.8
1233.8
0.56
5.15
22.76
57.64
0
0
0.0001
0
13.87
3966.4
Transit (Spain–Italy)
15
30
45
∆ Value
∆ Value
∆
- 35.3 1826.4
- 35.3 1820.8
- 35.6
- 33.9 1890.7
- 34.0 1884.7
- 34.2
- 30.8 1752.2
- 30.9 1743.9
- 31.3
- 2.9 2306.5
- 3.2 2287.0
- 4.0
- 28.9 1592.6
- 29.1 1586.6
- 29.4
- 39.7 1400.7
- 39.5 1386.1
- 40.2
- 3150.4
- 3911.2
- 1231.1
- 1225.3
+ 700.0
0.47 + 683.3
0.37 + 640.0
+ 625.4
4.30 + 628.8
3.41 + 641.3
+ 346.3
19.00 + 352.4
15.02 + 355.2
- 27.1
48.08
- 26.2
38.15
- 25.5
- 100
0
- 100
0
- 100
- 100 0.0002
- 77.8
0
- 100
0.007
- 0.0048
0
0
- 7.5
28.16
- 6.1
43.06
- 4.3
+ 5.3 2150.5
+ 10.6 789.70
+ 39.3
Table 12: Equilibrium outcomes after introduction of the new transalpine rail link operated by both an incumbent and a low cost rail
carrier
5
Cost–benefit analysis
For cost–benefit ratios (CBR) to become useful measures we should be able to
compare two rival projects CBR’s evaluated under the same method of scrutiny.
For a carefully done example on rail and road infrastructure see Affuso et al.
(2003). In the present case this could mean comparing the CBR of the Lyon–
Turin link to that of a proposed link crossing the Pyrenees. Within the scope of
this paper we will have to content ourselves with skipping this direct comparison
and, for now, see our results to serve as an indicator and as a first basis of
discussion. Since an equivalent data set to the one we used is available for
trans–Pyrenean freight traffic, such a comparison is a feasible task for the future.
5.1
Change in consumer surplus — Actual and potential users
of infrastructure
Consumer surplus CS has been obtained from Equation (17) in the Appendix
A. It represents a consumer’s surplus incurred by her choice set of available
alternatives per unit of 20 tons to be shipped. In Equation (11) we compute, as
a preliminary exercise, the aggregate gain in consumer surplus on the 2 markets
considered, Lyon–Turin Inter–Regional and Spain–Italy Transit, in the current
year. The total number of shipped units in market k in the relevant year t
is represented by qkt . λ accounts for the increase in market size due to the
introduction of the new link.
X
∆CSkt = (λCSkt,N ewLink
−
CSkt,StatusQuo ) qkt
(11)
Table 13: Increase in aggregate consumer surplus by market size scenario, in
Mio. Euros
1
2
1
2
operator on new link
operators on new link
operator on new link
operators on new link
Share of outside good
Lyon–Turin
Lyon–Turin
Spain–Italy
Spain–Italy
27
15%
13.7
13.9
83.7
83.7
30%
11.4
11.7
87.5
87.4
45%
1.9
2.3
83.6
83.6
5.2
Present Values
To obtain a first impression of the values that will have to be put into relation
with cost values30 we present here the present values of the aggregate gain in CS
over 30 years. Tables 14, 15, and 16 show the respective values for all market
size scenarios and for discount rates of 2%, 5%, and 8%31 .
Table 14: Present Value of increase in aggregate consumer surplus by market
size scenario, in Mio. Euros, over 30 years with discount rate of 2%
1
2
1
2
operator on new link
operators on new link
operator on new link
operators on new link
Share of outside good
Lyon–Turin
Lyon–Turin
Spain–Italy
Spain–Italy
15%
306.4
310.8
1 875.2
1 874.4
30%
255.8
261.5
1 960.4
1 957.3
45%
42.2
50.4
1 873.2
1 872.3
Table 15: Present Value of increase in aggregate consumer surplus by market
size scenario, in Mio. Euros, over 30 years with discount rate of 5%
1
2
1
2
operator on new link
operators on new link
operator on new link
operators on new link
Share of outside good
Lyon–Turin
Lyon–Turin
Spain–Italy
Spain–Italy
15%
210.3
213.3
1 287.1
1 286.5
30%
175.5
179.5
1 345.6
1 343.5
45%
29.0
34.6
1 285.7
1 285.1
Table 16: Present Value of increase in aggregate consumer surplus by market
size scenario, in Mio. Euros, over 30 years with discount rate of 8%
1
2
1
2
operator on new link
operators on new link
operator on new link
operators on new link
Share of outside good
Lyon–Turin
Lyon–Turin
Spain–Italy
Spain–Italy
30
15%
154.0
156.2
942.6
942.2
30%
128.6
131.4
985.4
983.9
45%
21.2
25.3
941.6
941.2
Cost of construction for the French, Italian, and the joint sections amount to approximately 13 000 Mio Euros.
31
Note, for example, that Affuso et al. (2003) use the official rate of 6% for the UK which
they state has been reduced to 3.5% recently.
28
6
Conclusion
The model used in this paper allows us to derive demand and supply equilibrium
behavior in a market with product differentiation. We apply this model to
the alpine freight transport market with differentiated ‘mode & alpine path’
products, in order to test the competitive viability of the ‘Liaison Ferroviaire
Lyon–Turin’ project.
As a first structural result we find a very low substitutability between freight
transport products, despite their heterogeneity beyond the mere modal split.
The shippers’ decision remains largely based on the mode choice. Thus, in
this compartmented market, rail alternatives, including the new high quality
one, do not appear competitive relative to road options. This demand rigidity
raises a methodological problem: mean utility specification demands a deeper
knowledge of shippers’ choice criteria. Micro–level data – collected during face–
to–face interviews for instance – would be of great help in this respect. Precise
and decisive–for–modal–shift criteria could be revealed to policy makers this
way and appropriate measures undertaken.
In this direction, improving the approximation of the product flexibility –
so far only captured by a road dummy in our mean utility specification – should
take an important part in further studies on the subject. As a matter of fact
we think the most obvious drawback of rail freight transport is its rigidity:
choosing rail in Lyon excludes changing modes until Turin. In this respect
inter–modality seems to be the key component of a competitive rail product32 .
Another improvement would be to simulate dynamic competition. Introducing
the new high quality link and the simultaneous entry of the incumbent freight
carrier and a low cost one, we do not observe any adjustment in shippers’
behavior. A sequential entry of the new link, first by the incumbent, then by a
low cost competitor, could have a deeper impact on shippers’ mode decision.
Current work on this paper includes application of the model on the more
recent and better CAFT 2004 data set and the embedding of the current equilibrium analysis into a CBA framework including externalities.
32
See Ruesch (2001).
29
A
Simulation of the entry of a new transport link
Once the model is calibrated we can proceed to the simulation of the entry
of a new alternative. Since we know the new alternative’s quality characteristics and have previously derived the coefficients of quality components in the
quality index we can easily obtain the quality index for the new alternatives
and therefore Vj . We now need to find out the pricing behavior of the freight
carriers when a new competitor arrives. We do this using the pricing Equation
(10) and the following expressions for the alternatives’ market shares that incorporate the quality index in the nested logit setting (see e.g. Clerides (2005)
or Trajtenberg (1989)):
First, define:
Dg =
X
Vj
e 1−σ
(12)
j∈Jg
Then, we obtain:
Intra–group market share:
Vj
sj/g
e 1−σ
=
Dg
(13)
Group market share:
(1−σ)
Dg
sg = P (1−σ)
g Dg
(14)
Total market share:
Vj
e 1−σ
hP
i
sj = sj/g sg =
(1−σ)
Dgσ
D
g g
(15)
Share of the Outside good:
s0 = P
g
1
(1−σ)
(16)
Dg
We solve Equation (10) for the new price vector p and obtain then the new
market shares using the above expressions, which is straight forward.
Disposing of prices and mean utility values after the introduction of new alternatives we can furthermore compare the net consumer surplus the decision
30
maker faces before and after the introduction of a new alternative. We take the
expression in Ivaldi, M. and F. Verboven (2001):


G
X
1
CS = ln
Dg(1−σ) 
α
(17)
g=1
B
European transport and the role of the Alpine
Arch
B.1
Major trends in European transport
The globalisation of the economy and especially the increasing integration of the
European economies have led to a considerable growth of the entire transport
sector, for both passengers and freight. A look at the development of intra–EU
Export–Import intensities between 1991 and 2006 in Figure 2 underlines this
trend.
Figure 2: Export and import intensities in Europe, between 1991 and 2004
Source: Eurostat, Values of 2004 and later are estimates
However, this spectacular growth has not been to the benefit of all the
different modes of transport. In the freight transport sector, road transport
showed the strongest growth with an increase of 177% between 1970 and 2000,
passing from 487 000 Mio tkm to 1 348 000 Mio tkm. In 2000, shares of rail and
road freight transport in Europe were 14% and 74%, respectively (See Figure
31
Figure 3: Modal Split in Freight Transport in the EU–15 in 2000 (Mio tkm)
Source: Eurostat
3).
In the passenger transport sector, road traffic accounts for the most significant growth rates among ground transportation modes. Between 1970 and
2000, passenger traffic rose from 1 851 000 million passenger kilometers33 (pkm)
to 4 202 000 Mio pkm, being an increase of 127%. In the same time period,
train passenger transport rose only by 38%, from 219 000 million pkm to 303
000 Mio pkm.
B.2
A parallel evolution of freight and passenger traffic on the
Alpine Arch
To start out with a definition, what is the ‘Alpine Arch’ ? It is the zone that
identifies alp–crossing traffic and varies in size based on the definition of its
endpoints, e.g. from the most south–western point at Vintimille to the most
eastern point at Vienna. The definition of its size therefore defines which O–D
pairs may be included by looking at only a limited array of passages. For the
analysis of the Lyon–Turin transalpine link project we restrict our attention
to the Western quarter of segment C in Figure 5, including only the French–
Italian alpine passages. This limitation simplifies the analysis very much even
though it excludes the Swiss and Austrian alpine crossings that might be in
competition with the above34 . Figure 6, however, shows that only a small part
of this traffic used Swiss passages in 1999.
33
34
Cars and busses.
E.g. for traffic between Northern France and Italy.
32
Figure 4: Passenger Transport Development in EU–15 and CH – 1990 values
normalized to 100
Source: Eurostat
Figure 5: The Alpine Arch
Source: ARE/VK, Berne 02.09.2004
33
Figure 6: Shares of Alpine passages of total French–Italian Freight traffic in
1999
Source: CAFT database 1999
The evolution of freight and passenger transport through the alpine arch follows perfectly the development in the European Union described above: global
traffic increase, more on the road than on rail. In 2003, 103.9 Mt of goods were
transported on road and rail through the alpine segment between the Mont–
Cenis / Fr´ejus and the Brenner in Austria. That means a doubling of the
transport volume within 20 years. The share of rail freight transport through
the segment A amounts to 37.1% and remained constant compared to 2002.
Out of the total freight traffic through the segment A, transit accounted
for 67%. The corresponding shares by country are 31.5% in France, 77.8%
in Switzerland, and 88.1% in Austria. Obviously, transit traffic is much more
important in Switzerland and Austria than in France, where 55% of freight
traffic are between France itself and Italy (See Figure 9).
Out of all freight transport in 1999, French–Italian Alpine crossings made
up for 44%, followed by Switzerland (39%) and Austria (17%). Figure 9 shows
the distribution of freight transport crossing the Alps at the French–Italian
borderline by main O–D countries:
As clearly illustrated in the above Graph 8, more than 92% of all freight
traffic crossing the French–Italian Alps either started or ended its journey in
Italy. The small rest that remains accounted for transit going and coming
through Italy and France, e.g. to the Balkans and Spain.
34
Figure 7: Relative shares of rail and road freight transport in the Alpine arch
Source: Alpinfo 2003
Figure 8: Shares of transalpine freight transport by country of alpine crossing,
1999
Source: CAFT database 1999
35
Figure 9: Shares of transalpine freight transport on French–Italian passages by
countries of Origin and Destination, 1999
Source: CAFT database 1999
With regard to passenger transport, between 1996 and 2001 the total number of travelers coming through the Alps has been increasing by 5%. This
increase was mostly borne by road traffic, which increased by 2.6% per year.
B.3
Transalpine transport infrastructure projects - A timeline
of capacity expansions
B.3.1
Austria
Even though Austria is rather on the eastern corridor and will, for example,
not attract traffic between Spain and Northern Italy, we will name here the two
existing major ways to cross the Alps in the western part of Austria:
• Reschen (Road)
• Brenner (Road and Rail)
For the Brenner, a new base tunnel at a length of 55km will be built with a
completion goal of 2012. This new high–speed rail link will permit travel speeds
by rail of up to 250 km/h and transit times between Munich and Verona of 2
hours and 19 minutes for passengers, and 3 hours for freight.
36
B.3.2
France
The main existing French alpine crossings are:
• Mont Blanc (Road)
• Mont Cenis / Fr´ejus (Road and Rail)
• Col de Montgen`evre (Road)
• Vintimille (Road and Rail, direct link on Mediterranean coastal line)
Even traffic from Mediterranean locations may be induced by the first two,
northern passages, due to higher travel speeds on these. Train passenger flows
coming from Marseille already pass through Chambry on their way to Northern
Italy and back. The only major infrastructure project crossing the French Alps
is the rail link between Lyon and Turin studied in this text. The opening of this
link is planned for 2013 and would allow travel times between Paris and Milan
of 3 hours and 40 minutes. In a larger European perspective, this connection is
part of a link from the Iberic peninsula to the southeastern European corridor,
via Trieste.
B.3.3
Switzerland
The most active role in enhancing transalpine transport infrastructure is played
by Switzerland. The major existing ways to cross the Alps through the central
alpine country are:
• Great St. Bernard (Road)
• L¨otschberg/Simplon (Road and Rail)
• St. Gotthard (Road and Rail)
• San Bernadino (Road)
In 1992, the NEAT35 concept was agreed upon by referendum. It is part of
the Overland Transport Agreement between Switzerland and the EU. The plan
includes the L¨otschberg tunnel (36,4 km) and the St. Gotthard tunnel (57 km),
both rail tunnels, which will be open for traffic in 2007 and 2014 respectively.
35
For further details see http://www.mjconstruct.com/tunnel/archive/2003/nov/the%20neat%20solution.pdf.
37
Once these major projects as well as several other planned infrastructure improvements are completed, travel times between Stuttgart and Milan will be
reduced to about 5 hours. A second road tunnel in the St. Gotthard is still
under discussion and, if constructed, may be open to traffic by 2020.
B.3.4
The Lyon–Turin transalpine rail link project
Since 1991, the project of a new rail link between France and Italy has been
under discussion. The project was part of the general scheme of high–speed rail
connections of 1992. It since stood to the benefit from the strong support of
the European Union. Rooted in the general scheme of the European network
of high–speed trains that was adopted by the European Council in 1990 it was
also added to the 14 priority transport infrastructure projects at the European
Council in Essen 1994. Furthermore, it has been emphasized in the White Book
of the European Commission published in September 2001 due to its potential
contribution to the infrastructure carrying intra–community traffic flows as well
as the modal switch in alpine valleys.
The project’s main objectives
In its original format, the project was projected for both freight and passengers:
• For passengers
– It aims to create substantial gains in travel time between destinations
in Northern Italy and French cities in the Alpine region, notably
Chamb´ery. In line with this, the progressive construction of a High–
Speed Rail (HSR) link between Lyon and Turin is planned. This
HSR link would make use of a border–crossing ‘base tunnel’ with 52
km of length and several further projects on Italian territory linking
the base tunnel to the historic as well as the new line in the Susa
Valley close to Bruzolo;
• For freight36
36
3 kinds of rail freight transport exist: Classic = complete trains linking 2 loading/unloading stations, generally heavy goods transports / Combined = Intermodal system
where goods are transported on trucks as well as train wagons on the overall journey / Piggyback (‘Autoroute ferroviaire’) = Entire truck is loaded on special trains.
38
– The goal is to allow rail transport to play a more important role in
freight transport by offering a substantial level of capacity (40 Mt
per year) once the project in its entirety is completed. In order to
do this, the realization of a high–performance track, using the same
above–mentioned base tunnel as well as existing and new tracks to
access the latter is projected.
The actors involved in the project
From an institutional point of view, since January 1996, the project has
been administered by a French–Italian inter–governmental commission (CIG).
It is composed by three sections for both passengers and freight; each has been
delegated to a different project planner:
• The French part, going from the urban area of Lyon to the valley of Saint
Jean de Maurienne, has been delegated to the RFF37
• The international part, comprising the 52 km long base tunnel between
Saint–Jean de Maurienne and the valley of Susa, has been delegated to
the company Lyon–Turin Ferroviaire (LTF)38 , which is owned equally by
RFF and RFI39
• The Italian part, from Bruzolo to the urban area of Turin, has been
delegated to RFI.
Timeline and prospected costs
Since January 29th, 2001, the project has been part of an international treaty
that has been ratified by the French and Italian parliaments in 2002. This treaty
includes a precise description of the operations to be undertaken but leaves a
certain degree of flexibility with regard to the date of their realization while
envisioning the beginning of transport services around 2015. Really, the first
article stipulates that ‘the start of services should be realized at the date of
saturation of existing infrastructure.’ Even though the treaty merely imposes a
compulsory directive with regard to the period of investigative studies, a more
voluntary aim has been established at the P´erigueux summit on November 27th
2001, bringing forward the prospected start of services on the Lyon–Turin link
to 2012.
37
R´eseau Ferr´e de France
LTF replaced GEIE Alpetunnel (‘Groupement Europ´een dInt´erˆet Economique’) in
September 2001.
39
Rete Ferroviara Italiana
38
39
In December 2003, the European Council has approved the participation of
the EU, carrying 20% of the financial burden of the construction of the border–
crossing part of the link. In May 2004, France and Italy signed a ‘Memorandum
d’entente’ stipulating that both countries contribute two equal shares to the
amount of investment needed for the main part of the link. After deduction
of the EU’s share each country will have to contribute 5.2 Billion Euros to the
project.
40
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