Summary
This paper aims at determining the optimum viable solution of an investment on a
suburban coastal shipping system in the area of Athens. More specifically it refers to
the development of a sea transport system, alternative to the existing road one, that
would connect Piraeus with the southern suburban coastal area of Athens. The best
viable solution of such an undertaking is considered to be the one, which under the
existing constraints maximises the total profit that derives from this investment. The
variables used for the formation of the constraints are the number of vessels used, the
routing and the price of the services.
The article after presenting the methodology of
the market analysis, focuses on the financing alternatives of the project and their
impact on its economic efficiency and concludes with the best viable scenarios and
optimum solution.
1. Introduction
The paper’s main objective is to determine the optimum solution of an investment
undertaking that includes an alternative transportation system, that is a suburban
coastal transport system in the south region of Athens. Such a system is believed that
can offer a possible solution to the transport problems of congestion, which
characterise the existing overloaded road network of Athens.
Suburban coastal shipping has a special character. First there are not many examples
of cities that use an analogous transport system.
Secondly, the main competitor of sea
transport is road transport or fixed track systems (train, tram). It should be mentioned
that the current transportation system despite its deficiencies in terms of time, cost as
well as externalities remains a strong travel alternative and consequently a constraint
for the development of the proposed seaborne transport. This is due to the several
advantages it offers to its users like door to door services, flexibility and speed. City of
Athens disposes a road system as the main one satisfying the transport demand to and
from the suburban areas.
The best economic solution for an investment of this kind is determined as the one that
maximises the profit of the investor, under the constraints that the current transport
system sets. In this context, in order to format the constrains of the system, a market
analysis has been conducted taking all the above into consideration in order to
examine the possible demand for such a system versus the existing road transport one
(Greek Bank of Industrial Development, 1995).
However, the question is whether an
enterprise in this field would be economically viable and which would be the required
demand of that service, in order to be able to equalize the marginal costs with the
marginal revenue.
2. A methodology for the development of a suburban coastal transportation
system in Athens urban area
2. 1 The survey
It is obvious that the greatest part of the demand for sea transportation would come
from the coastal suburbs that lie mostly in the eastern and southern part of Piraeus and
their most important feature is that they constitute areas of permanent residence.
These suburbs include A limos, Voila, Vouliagmeni, Glyfada, Elli niko, Kali thea,
Moschato, Piraeus and P. Faliro.
They constitute the districts of direct interest for this
analysis (see figure 1). The case of the nearby districts that are situated northern than
the coastal ones is also examined (Ag. Dimitrios, Argyroupolis, Vari, Ilioupolis,
Kou kaki, N. Smirni etc). This case obviously refers to the current car owners who can
drive their cars up to the terminal station and then embark on the sea vessels after
parking their cars (park and ride).
The first step in estimating the potential demand for a suburban coastal transportation
system was to proceed to the determination of the total movements in the above
mentioned area.
This led to the development of the analogous Trip Distribution Model
(Sambracos 2001) that shows the movements between the examined suburbs by
private car and by bus. Data for the creation of the model was gathered by several
origin – destination surveys that have taken place over the last years in addition with
the records of the National Statistics Services on the population of the suburbs.
Figure 1: Suburban coastal shipping operation in the south region of Athens
The second step was to proceed to a Modal Split Model (Abacoumcin 1990,
Giannopoulos 1981) and to determine the alternative means of transport that could
satisfy the transport demand in and between the suburbs in question. In order to create
such a model, the ‘stated preference’ method was used, a technique that uses and
analyses the alternative scenarios, the questioned passengers state that they would
prefer (Pearmain et.
al, 1990, Bristow et. al, 2000). The questioned person is asked to
choose between alternative situations / solutions , some of which may not yet be known
to him, such as the proposed coastal transport system. The reason for using this
method is mainly the fact that it can show results based on small samples (Koppelman
et al, 1983).
Thus the classic revealed preference method show disadvantages since it
cannot be used in demand forecasting under existing conditions and cannot use
qualitative criteria (such as the comfort of a transport means).
The survey was conducted with the use of questionnaires on June 1994 in the two
main centres of interest, Piraeus and Glyfada. The questionnaire included 13 questions
that had to do with:
– the used transport means,
– the origin and destination of the trip,
– the purpose of the trip,
– the frequency of the movement,
– the preference towards sea transport,
– the ability to use a car,
– the minimum and maximum time for the users of private cars and buses,
– the parking fees,
– the walking distance between the parking area or the bus station and the final
destination.
Besides the questions there were six different scenarios, that referred to combinations
of four main factors that determine the choice of a transport means. These are:
– the time of the trip (from door to door),
– the price of the ticket,
– the possible delays in the arrival of the vessel,
– the sea / weather condition.
Additionally, data regarding the sex, age and other demographic characteristics were
taken into consideration.
In order to determine the demand allocation between the alternative transport means
the Logit analysis was conducted (Ortuzar et. al. , 1990) using as inputs the mentioned
six scenarios and as variables the time of the trip (by bus / car and ship), the cost of the
trip (by bus / car and ship), the delays (by ship), and the weather conditions of the
coastal shipping alternative as well as the characteristics of the users (age, sex,
purpose of the trip etc). Above them the time, cost of the trip and the weather
conditions were statistically the most important variables.
2. 2.
Designing the system
In order to design the suburban coastal shipping system there are several factors of
both quantitative and qualitative character to consider, using as data the outputs of the
origin-destination model and the conducted survey (stated preferences).
The first factor was the ports that should be used to format the system, the number of
stops between the main ports of Piraeus and Glyfada in combination with several
levels of ticket prices. Three scenarios were examined:
1. Piraeus port – P. Faliro – Glyfada
2. Piraeus port – Zea port – P.
Faliro – Glyfada and
3. Piraeus – Glyfada
The results from the survey showed that more mid stops would make the system
unattractive and inefficient compared to the existing road one since they would
increase the travel time. Additionally, more vessels would be required increasing
therefore the cost of the system.
The price levels examined for each scenario referred to a sea fare of 300, 400, 500,
600 and 700 GRDrh respectively. The results of the stated preference model for a 12
hour and a peak hour transport work showed that the higher the number of mid stops
the higher the demand for travelling. Based in that conclusion the third alternative was
abandoned.
The second factor was the vessels type and the necessary number of vessels to cover
the two and three mid stops alternatives. The number of vessels depends on the
demand for each alternative on a 12 h and peak hour (PD) basis, the time of the trip and
consequently the maximum number of trips the vessel can perform (FB), the
frequency of embarkations (FL), the capacity of the vessel (C = PD/FL) and equals to
the ratio:
FB
FL
n =
As for the type of the vessels, the choice was based on four factors:
– the purchase and operational cost
– the operational reliability, speed and flexibility
– the friendliness towards the environment
– the safety and comfort for the passenger
The proposed type of the vessel based on the above factors was the flying dolphin
type. Its characteristics on speed (33 knots / h ) and capacity (110 pas. ) prove that for the
one mid stop scenario two vessels are requested and for the two mid stop scenario
three vessels.
3.
Defining the optimum solution
The best viable solution of such an undertaking is determined as the solution that
maximises the Profit (P) of the undertaker. According to the economic theory
maximising the Profit means maximising the difference between Revenues (R) and the
Cost (C) that derive form the operation of the company. In other words it is:
P = R-C
max (P) = max (R-C)
The constraints of the above profit maximization consist of the existing alternative
solutions, which derive from the market analysis on demand patterns towards the
undertaking.
3. 1 Cost of the suburban coastal transport system
The total cost, that such a transport company will face is a function of (Karvounis
2000:
– the investment cost (i),
– the operating cost (o)
– the financial cost (m)
– the depreciation cost (d)
Therefore we have,
C = f (i, o, m, d)
The total cost depends on the number of stops the vessels make. The more the stops
the bigger the roundtrip time and consequently a bigger number of vessels is required
in order to maintain a high level of service.
This increases the investment cost since
more capital is required to cover not only the acquisition of the vessels but also their
operating expenses. Therefore, it is essential to determine the level up to which we
must invest. There are different alternative scenarios that are based on the market
analysis on origin – destination in order to determine the best alternatives for this
system. The market survey already indicated two main alternatives using as a variable
(i) the number of stops and the number of vessels to be employed:
o Scenario 1: one stop between the ports of origin and destination, using two vessels
on pick hours
o Scenario 2: two stops between the ports of origin and destination, using three
vessels on pick hours
That is Si, where i = 1, 2 stops
Considering these scenarios, the main cost elements are estimated as following:
a. Investment and operating costs
The estimation of the investment and operating costs is presented in the table 1 below.
The investment cost consists of Founding & Organisation cost, Fixed investments
(facilities etc), Working Capital, Unforseen expenses
The operating cost consists of Fuels, lubricants etc, Personnel, Office expenses,
Maintenance Expenses, Port duties, Insurance, Advertisement
Table: 1 Investment and operating cost for the two scenarios (in 000 GRDrh)
Costs Scenario 1 Scenario 2
a.
Total investment costs 1. 150. 000 1. 500. 000
Founding – Organization 10.
000 10. 000
Fixed investments 940. 000 1. 240. 000
Starting Capital 187. 367 243.
100
Unforeseen expenses 12. 633 6. 900
b. Total Annual Operating Cost 562. 100 729. 300
Fuels 281.
000 337. 000
Personnel 130. 000 182. 000
Office expenses 3. 000 3.
000
Maintenance Expenses 13. 500 18. 000
Port duties 45. 000 80. 000
Insurance 13. 500 18.
000
Advertisement 25. 000 25. 000
Unforeseen 51. 100 66.
300
The estimation of the costs is based on market prices as of 1995
b. Depreciation cost
The cost of depreciation is estimated for both scenarios, using the depreciation rates,
4% for building and offices, 10% for the vessels and 20% for the rest equipment, as
following:
o Scenario 1: annual depreciation for the first five years is 93, 2 mil. GRDrh and for
the other five 91, 2 mil. GRDrh.
o Scenario 2: annual depreciation for the first five years is 123, 2 mil. GRDrh and for
the other five 121, 2 mil.
GRDrh.
c. Financial cost
The financial cost for the development of this transport system deals with the
financing of the investment costs for its development. Several scenarios of financing
are proposed that include:
– different % of loaning and own capital
– several levels of loaning interest
3. 2 Revenues of a suburban coastal transport system
The revenues from the operation of this system are a function of the demand (q) and
the price of the ticket (p).
R = f (p, q)
The level of demand and the possible ticket price levels were examined during the
market research.
Although all price levels are possible the research showed that a
range between 500 GRDrh and 700 GRDrh is the most viable one. Above this level the
demand is low in favour of road transport and below it is economically unprofitable
for the company. The two scenarios therefore are:
Sj, where j = 700 GRDrh, 500 GRDrh
It is assumed that for the working days the total revenues are:
2 x ‘I x ~N
where, ~N is the price of the ticket and
‘I the corresponding total daily number of passengers.
For the weekends (2 X 52 = 104 days per year) the passenger traffic is estimated to be
70% of the traffic during working days. Therefore the total annual income is:
(261+0, 7 x 104) x 2 x ‘I x ~N = 667, 6 x ‘I x ~N
Using the above mentioned scenarios Si and Sj, four cases result:
-Case ‘A: S 1, 700 (1 mid-stop & price of the ticket 700 GRDrh)
-Case ^A: S 2, 700 (2 mid-stops & price of the ticket 700 GRDrh)
-Case C: S 1, 500 (1 mid-stop & price of the ticket 500 GRDrh)
-Case D: S 2, 500 (2 mid-stops & price of the ticket 500 GRDrh)
Taking the above into consideration the total annual revenues are for each case:
– S 1, 700: 913 mil. GRDrh.
– S 2, 700: 961 mil. GRDrh.
– S 1, 500: 828 mil. GRDrh.
– S 2, 500: 875 mil.
GRDrh.
In order to forecast the total revenues for the years to come there is the assumption that
the traffic natural increase rate is 1, 5% annually, therefore all the above amounts for
the future year e should be multiplied by 1, 015 (e-1) (e = 1, the first year of evaluation).
4. Financial analysis of the system
4. 1 Evaluation of the financial cost
The determination of the best financial solution that maximises the profit of the
Coastal Transport Company is the next step of the investment evaluation of the
system. The financial cost (m) of the undertaking is determined by two variables:
– l: the percentage (%) of a loan to cover the investment cost and
– n: the level of the interest rate
Therefore m is a function of l and n, or
m = f (l, n)
and so C = f (i, o, l, n, d)
All cost components have been determined for all Sij.
Assuming that the variables in
the above function are l and n, we will try to maximise it by using all possible
combinations of l and n.
In order to evaluate the financial efficiency (Goulielmos 1997) based on the above
variables of this initiative we use the method of the Net Present Value and the Internal
Rate of Return. Using a discount factor of 4%, the target is to find the alternative Sij
that gives the higher Net Present Value (NPV) as well as IRR.
Using the above variables we proceed to a 10 years simulation in order to format the
equation that proves the relationship between the percentage of loan for all possible
levels of loan interest and the NPV, IRR results. The results for all cases and the final
equations are included for each case in table 2. The general form of the derived
equations, as resulted from the simulation, is a first degree one, with the following
formation:
Y = a – bX
Where Y = NPV
X = the percentage of loan at different levels of interest rate
a, b = constants
The main conclusion is that S 1, 700 is the optimum solution, since it shows the greatest
Net Present Value and Internal Rate of Return for all different levels of interest (Table
2).
Regarding the financing of the project, it is concluded that in every level of the
interest rate, the best solution is to self-finance the project.
Another important observation is that in S 1, 700, the percentage of loan that makes the
NPV equal or less than zero (which means that the project is economically a non
viable solution) is the highest, in comparison with the other Cases. For example for
S 1, 700 (see table 2 and figure 2), in the case of a loan interest of 4% the company can
take a loan that does not exceed 97, 62% of the investment cost. In all other cases, this
percentage is much lower, which means bigger financial risk and danger, in the case of
inability to self-finance a significant proportion of the investment cost. The same
conclusion can be derived for all levels of interest loan.
Taking the above into
consideration, the second viable solution is S 1, 500, where for a 4% of loan interest, the
total loan must not exceed 70, 03% of the investment cost.
Table 2: Financial viability of the project for Sij
S 1, 700 S 2, 700
Loaning
Interest
Rate
Equation
(y = a-bX)
% of Loan
that makes
NPV = 0
Equation
(y = a-bX)
% of Loan
that makes
NPV = 0
2% Y = 2. 352. 749-2. 363. 779 X 99, 53% y = 1.
299. 424-3. 083. 191 X 42, 14%
4% Y = 2. 352.
749-2. 410. 128 X 97, 62% y = 1. 299. 424-3. 143.
645 X 41, 33%
6% Y = 2. 352. 749-2. 456. 477 X 95, 78% y = 1. 299.
424-3. 204. 100 X 40, 55%
8% Y = 2. 352. 749-2.
502. 825 X 94, 00% y = 1. 299. 424-3. 264. 555 X 39, 80%
10% y = 2.
352. 749-2. 549. 174 X 92, 29% y = 1. 299. 424-3.
325. 009 X 39, 08%
15% y = 2. 352. 749-2.
665. 045 X 88, 28% y = 1. 299. 424-3. 476. 146 X 37, 38%
S 1, 500 S 2, 500
Loaning
Interest
Rate
Equation
(y = a-bX)
% of Loan
that makes
NPV = 0
Equation
(y = a-bX)
% of Loan
that makes
NPV = 0
2% y = 1.
687. 936 – 2. 363. 780 X 71, 41% y = 626. 789 – 3.
083. 191 X 20, 33%
4% y = 1. 687. 936 – 2. 410. 128 X 70, 03% y = 626.
789 – 3. 143. 645 X 19, 9%
6% y = 1. 687. 936 – 2. 456.
477 X 68, 71% y = 626. 789 – 3. 204. 100 X 19, 56%
8% y = 1.
687. 936 – 2. 502. 825 X 67, 44% y = 626. 789 – 3. 264.
555 X 19, 20%
10% y = 1. 687. 936 – 2. 549. 174 X 66, 21% y = 626. 789 – 3.
325. 009 X 18, 85%
15% y = 1. 687. 936 – 2. 665.
045 X 63, 34% y = 626. 789 – 3. 476. 146 X 18, 03%
Figure 2: Financial viability of the project
4. 2 Sensitivity analysis
Another important relationship is observed between the level of the loan and the
economic effectiveness of the project (Theofanides, 1985). It is also essential to
recognise the risk of such an investment (we choose the optimum solution S 1, 700).
Since
the calculation of the costs is based on real data, while the approach of the revenues is
based on demand forecasts, a sensitivity analysis is performed, on different levels of
demand. The results are presented in figure 3.
Figure 3 shows that, the lower the loan, the lower the danger for all different levels of
demand, which we normally expected. A 10% reduction in revenues has no impact on
the viability of the project.
On the other hand a loan of 30% or more of the investment
cost shows great sensitivity if the revenues reduce for 20% and higher.
Loan Interest and % of Loan
0, 00%
20, 00%
40, 00%
60, 00%
80, 00%
100, 00%
2% 4% 6% 8% 10% 15%
Loan Interest
% of Loan
Case A
Case B
Case C
Case D
Figure 3: Sensitivity analysis for S 1, 700
5. Conclusion
From the above analysis it is concluded that the implementation of a suburban coastal
transport system in the southern Athens area is economically viable. This can be
achieved with one or two mid stops and different price ticket levels.
The optimum
though economic solution is accomplished when we apply the smallest number of mid
stops and the higher ticket price level. In the examined case study, an one mid stop
system with a 700 GRDRh ticket (S 1, 700), is the most efficient financial solution since
it shows the higher NPV and IRR. That means that the investor has bigger profit by
offering the minimum service (1 mid. stop) with the highest price of the range
examined (500 – 700 GRDrh). If he increased the stops and offer more service, and if
he reduced the ticket price, then he would attract more customers.
But, according to
the above analysis the best solution is S 1, 700, that means less service, less passengers
and higher ticket price.
Thus, the percentage of Loan that makes NPV = 0 is higher in case S 1, 700, in every level
of loan interest. That means that offering lower service with the higher price holds
minimum financial risk for the investor. Additionally, the sensitivity analysis shows
that the higher the level of uncertainty in the total revenues (reduction in demand) the
lower the percentage of the loan, that the investor should take. From that point of view
the case S 1, 700, proves to be the most secure one.
Sensitivity Analysis
-4000000
-3000000
-2000000
-1000000
1000000
2000000
3000000
4000000
0 30% 50% 70% 100% % of Loan
NPV
40% reduction in Revenues 30% reduction in Revenues
20% reduction in Revenues 10% Reduction in Revenues
6.
References – Bibliography
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3.
Giannopoulos G. (1981) Transport Planning and Traffic techniques Athens
pp. 187-212
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pp.
358-365 (in Greek)
5. Greek Bank of Industrial Development (1994) Investigation of the operation of a
coastal shipping transport network between Piraeus and the South suburban
area”
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pp 723-740
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S. , Chu C. (1983) Effect of Sample Size on Disaggregated Choice
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G. (1990) Modeling Transport, John Wiley and Sons
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(2001) Introduction on Transport Economics Publ. Stamoulis,
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