In 2011 we presented a paper on "Daily Demand Distribution and Flight Programme Attractiveness for the Passengers on a City Pair". Despite a far better understanding on how demand and flight frequencies interact on a route, this approach had the major drawback of assuming an equal trip time delivered by all of the operators. This paper is aimed at overcoming this flaw and assessing how trip time differences affect the market share an airline can gain/lose on a route. Trip time related passenger demand is a topic mostly addressed by value-of-time theories. It is addressed by statistical methods too such as the so-called LEK arithmetic model which provides the market share of a high speed train services competing against an air one. Nevertheless, they cannot provide efficient enough tools for assessing the actual weight of the sole trip time factor on the passenger choice as, for instance, the fares, the frequent flyer programmes, departure/arrival times, flight positioning on the GDS screens do play a significant role too. This is why we keep favouring a behavioural approach which would be derived from the frequency attractiveness method we have already developed. We expect this approach to answer the following questions: 1/ what is the potential demand a non stop service can gain while passengers are offered multi-stops services only? 2/ how far a shorter transfer time affects the demand attractiveness of a hub? 4/ On long haul operations, does a slightly higher cruise speed provides a real demand advantage? 3/ what is the potential market share a high speed train service can gain over air services?

1. Daniel SALLIER 1
TRIP TIME DIFFERENCE & SHARE OF THE PASSENGER DEMAND ON A CITY PAIR
By Daniel SALLIER
Aéroports de Paris
Bât. 530 – Zone Orlytech
9, Allée Hélène Boucher
Orly Sud 103
94396 Orly Aérogare cedex
France
Telephone: +33 6 82 84 12 56
daniel.sallier@adp.fr

2. Daniel SALLIER 2
ABSTRACT
In 2011 we presented a paper on "Daily Demand Distribution and Flight Programme Attractiveness
for the Passengers on a City Pair". Despite a far better understanding on how demand and flight
frequencies interact on a route, this approach had the major drawback of assuming an equal trip
time delivered by all of the operators. This paper is aimed at overcoming this flaw and assessing
how trip time differences affect the market share an airline can gain/lose on a route.
Trip time related passenger demand is a topic mostly addressed by value-of-time theories. It is
addressed by statistical methods too such as the so-called LEK arithmetic model which provides the
market share of a high speed train services competing against an air one. Nevertheless, they cannot
provide efficient enough tools for assessing the actual weight of the sole trip time factor on the
passenger choice as, for instance, the fares, the frequent flyer programmes, departure/arrival times,
flight positioning on the GDS screens do play a significant role too. This is why we keep favouring
a behavioural approach which would be derived from the frequency attractiveness method we have
already developed.
We expect this approach to answer the following questions:
1/ what is the potential demand a non stop service can gain while passengers are offered multi-
stops services only?
2/ how far a shorter transfer time affects the demand attractiveness of a hub?
4/ On long haul operations, does a slightly higher cruise speed provides a real demand
advantage?
3/ what is the potential market share a high speed train service can gain over air services?
KEYWORDS:
demand, trip time, market share, cruise speed

3. Daniel SALLIER 3
1. INTRODUCTION
In 2011 for the annual ATRS meeting which took place in Sydney, we presented a behavioural
approach entitled: "Daily Demand Distribution and Flight Programme Attractiveness for the
Passengers on a City Pair".
The approach was based on two main assumptions:
1/ the ability to establish how the daily demand evolves over the day long as a function of
departure/arrival time attractiveness, trip time, local time difference between the departure and
arrival airports. The following chart represents such a daily demand evolution:
Figure 1: Example of daily demand distribution
2/ the assumption that each flight has a departure/arrival flight attractiveness curve attached,
which gives the percentage of the daily demand which keeps being attracted by this very flight
as a function of the time lag between the actual flight departure/arrival time and the desired
departure/arrival time. The following chart represents such a flight attractiveness function:

4. Daniel SALLIER 4
Figure 2: Example of flight attractiveness curves
The flight attractiveness curve can be truncated so that any value lower than a predefined minimum
threshold is forced to 0. Most of the time we consider a minimum threshold of 12.5% (12.5% = 1/8)
which is homogenous with the fact of using a set of 4 attractiveness index values for the
departing/arrival hours, so that the 0 to 12,5% band corresponds to the not attractive criteria.
The combination of daily demand curve and flight attractiveness function/envelop results in the part
of the daily demand which is attracted by a specific flight or a flight programme, the so called
demand coverage which is the area of the dark blue curve as illustrated hereafter.
Figure 3: example of daily demand, flight programme and demand coverage

5. Daniel SALLIER 5
In case of competition between different airlines on the same route, "we can assume that the
'instantaneous' number of passengers identified by their desired departure time t the airline can
capture is proportional to the airline coverage curve value for t over the sum of coverage curves of
all the competitors for t. If we sum up this number of "instantaneous" passengers along the
complete day over the total number of daily passenger we get the airline market share"1
:
0 1 2 3 4 5 6
0 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
1 100.0% 50.0% 23.3% 23.3% 19.2% 18.3% 18.3%
2 100.0% 76.7% 50.0% 42.3% 38.2% 37.4% 37.0%
3 100.0% 76.7% 57.7% 50.0% 45.9% 45.1% 44.3%
4 100.0% 80.8% 61.8% 54.1% 50.0% 48.9% 48.1%
5 100.0% 81.7% 62.6% 54.9% 51.1% 50.0% 49.2%
6 100.0% 81.7% 63.0% 55.7% 51.9% 50.8% 50.0%
Airline#2
Airline #1Daily no. of
flights
High yield market share of ariline #2
Table 1: Market share example
The major drawback of this approach comes from the assumption that all the contenders on a route
deliver the same or a very close trip times. This is a risky option to take in case of:
1/ non-stop flights versus hubed ones;
2/ competing airlines on the same city pair via different hubs having different transfer time;
3/ the market share and the economical return a hub may expect in investing in shortening the
passenger transfer time;
4/ rail/air competition on a route
The methodological option we adopted is to generalise the already existing approach we have
described in our last year paper. This will be done in three steps:
1/ how trip time difference does affect daily demand distribution potential?
2/ the concept of composite daily demand on a given route;
3/ market share between the different competitors resulting from the sole effect of trip time
difference;
1
In "Daily Demand Distribution and Flight Programme Attractiveness for the Passengers on a City Pair" by D.
Sallier, ATRS annual Symposium (Sydney 2011)

6. Daniel SALLIER 6
2. TRIP TIME DIFFERENCE AND PASSENGER DEMAND
We already know how trip time affects the daily demand pattern. The question left pending is, given
a flight, which part of the daily demand keeps being interested by a longer trip time flight?
The very question raised at this stage is on the direct effect of trip time difference on demand. It
does not include the burden of changing flight in a hub may have on the passenger demand.
The trip time difference issue should be split into two effects:
1/ the effect of trip time difference on passenger demand assumed that the customers are offered
as many flights along the day as they wish. It is this point which is addressed here;
2/ the effect of trip time difference on passenger demand which takes into consideration the actual
supply of flights by all the airlines. This point is addressed in the rest of the paper.
Let us assume that the shorter flight available on the market lasts 8 hours, which part of the daily
demand keeps being interested by a flight which would last 10 hours and 15 minute, let us say 2H15
more?
It is the flight attractiveness curve introduced in the method presented last year which provides the
answer:
Figure 4: Example of a 2H15 longer trip time on the residual demand

7. Daniel SALLIER 7
In this example of a flight leaving at 12H00 am, only 42% of the passengers who would like to
leave at 8H45 am keep being interested by this flight. Same thing for the passengers who would like
to leave at 2H15 pm.
A different way to read this chart is to consider that the customers who would like to start their trip
at 8H45 am would arrive 2H15 later than originally wished if they take the 12H00 am departing
flight. Conversely passengers who would like to arrive 2H15 later at destination have to take an
earlier flight. By some respects their journey would last 2 hours and 15 minutes more resulting in
42% only of the demand keeping being interested.
It means that, for a given city-pair, we have to take the shortest trip time as a reference which drives
the span of the flight attractiveness curve (the shorter the flight, the "slimmer" the curve), out of
which we can estimate which part of the daily demand keeps being interested by any flight having a
longer or equal trip time. The residual demand is calculated on the basis of flight attractiveness
function of the shortest flight available on the market.
In the case of our former example of a 1-stop flight of 10H15 trip time, only 42% of the daily
demand can be interested assumed that an 8H00 flight is available on the market. It is the residual
demand pattern:
Figure 5: Example of corrected daily demand distribution
The area of the dark blue curve is equal to 42% while that of the light blue one is equal to 100%.
3. COMPOSITE DAILY DEMAND PATTERNS
On the chart we can represent the different (residual) demand patterns of all the itineraries serviced
on the route as a function of the local departure time:

8. Daniel SALLIER 8
Figure 6: the 8H00 and 10H15 (residual) daily demand pattern
For instance, at 6H00 pm (18H00) there is about 10% of the number of daily passengers per hour
who would like to take a 8H00 long flight and 3,2% only who can consider having a 10H15 one. So
there is a maximum of 10% of the number of daily passengers per hour who would like to leave by
either one of the 2 available flights.
For instance at 1H00 am there is about 4% of the daily number of passengers per hour who would
like to leave (by the 10H15 trip time flight).
The composite daily demand pattern is defined as the envelop of the (residual) daily demand
patterns of all the flights itineraries which are servicing the considered city pair. This envelop
should be resized so that it area keeps being equal to 100%.
In addition, in the initial development we exposed last year the daily demand pattern shape is
exactly the same either stated in local departure or arrival time with the only difference of the
second being shifted by the apparent elapsed trip time. The trip time difference breaks the pattern
shape homogeneity between departure and arrival times. This illustrated in the following set of
charts.

9. Daniel SALLIER 9
Figure 7: Composite daily demand patterns
4. DEMAND COVERAGE AND MARKET SHARE

10. Daniel SALLIER 10
On our former paper we have defined the concept of a single flight or a flight programme coverage
which is generalised here on the basis of the residual demand curve. Based of the same former
example, let us suppose that the 8H00 long flight is leaving at 10H00 am and the 10H15, 1-stop
flight is leaving at 12H00 pm. The following chart represents both (residual) demand coverage of
each flight we later name flight/trip imprint together with the departure/arrival time related
composite demand pattern:
Figure 8: Departure/arrival time flight imprints and composite demand pattern

11. Daniel SALLIER 11
These charts show that the shorter flight get a far better demand coverage that the longer 1-stop one.
Exact figures are 26% of demand coverage for the 1st
one and 11% only for the second one.
However, despite a better demand coverage, the 1st
flight is very far from being attractive for most
of the passengers, a bit more that 70% of the potential daily customers finds this flight unattractive
not because of its trip time, but because of its departure/arrival time: unsatisfied customers for the
early 8H00 am departure peak, same for the evening 8H00 to 9H00 pm departure slot. It would not
come as a surprise that both flights may share very similar part of the market.
The market share calculation defined in our former paper was:
( )
( )
( )
23:59
,
,0:00
FP i
i
FP i
i
I t
S t dt
I t
δ= × ×∫ ∑ where
iS is the market share of airline i;
( ),FP iI t is the flight programme attractiveness function of the airline i;
( )tδ the relative demand density function
The same calculation rule will apply with two major differences:
( )
( )
( ) ( )
( )
( )
( )
23:59 23:59
, , , ,
, , , ,0:00 0:00
1FP D i FP A i
i D A
FP D i FP A i
i i
I t I t
S t dt t dt
I t I t
α δ α δ= × × × + − × × ×∫ ∫∑ ∑
Where
[ ]0,1α ∈ , the departure time weight
The subscribe D refers to departure time related functions (residual demand, flight attractiveness)
The subscribe A refers to arrival time related functions (residual demand, flight attractiveness)
If the sum of the flight programme attractiveness is null ( ), , 0FP D i
i
I t
 
= ÷
 
∑ or ( ), , 0FP A i
i
I t
 
= ÷
 
∑
then the attractiveness value of each flight is replaced by its demand correction factor (i.e. 100% for
the 8H00 long flight and 42% for the 10H15, 1-stop flight). This criterion just translates the idea
that if there is no flight at all attractive for the passengers willing to leave at a given time, then they
will tend to proportionally select those with the higher correction factor.
In our example we end up with a market share of 54% for the shorter flight and 46% for the longer
one according to this approach. If both two flights were to leave at 10H00 am, the market share
would be 52% vs 48%. If the second flight would have a 4 hours longer trip time, then the market
share split would be 70% vs 30%.

12. Daniel SALLIER 12
5. EXAMPLES
Demand potential of a non-stop flight: Lyon to New York
The city of Lyon is the second largest city in France after Paris. It is located in the Rhône valley
which is a highly industrialised area of the country. To make a long story short it is a populated and
rich area of France. This is the reason why non-stop service to New York have been operated in the
past … several times, but always failed to show profit making capabilities.
For the sake of illustration we will only consider the estimate traffic from Lyon to New York
carried by the three major alliances:
Figure 9: Lyon to New York flight supply by the alliances
In March 2012, based on the ADI database (Sabre Technology), an average number of 79 daily one
way O&D passengers are identified on the Lyon – New York route:
Table 2: Lyon to New York daily traffic
Here are the flight programmes offered by the 3 alliances:

13. Daniel SALLIER 13
Table 3: Lyon to New York flight supply
Using the approach detailed in this paper we estimate market shares of each competitor on the
market exclusively based on trip times and flight scheduling criteria which does not take into
consideration fares, frequent flyer programme, airline brand, product quality, etc…
We will consider 2 scenarios:
1/ an independent airline offers a non-stop service. They are 2 departures slots which will allow it
to maximise its market share: an 8H30 am or 5H30 pm departure from Lyon. This airline can
reach a 27% maximum market share (18 daily passengers one way) regardless of any further
demand stimulation by attractive fares;
2/ for operational reasons (turn around time issue in Lyon), only a US based airline can operate
this flight for they are no other long haul destinations serviced from Lyon. Let us suppose that
Delta Airline, member of the Skyteam alliance opens the non-stop route at the same departure
time from Lyon as one in the former scenario.
The following table provides:
1/ the actual market share;
2/ the estimate market share based on our approach which does not take into consideration fare
difference, product quality, airline brand, etc…;
3/ change in market share for scenario # 1;
4/ change in market share for scenario # 2.

14. Daniel SALLIER 14
Table 4: Non-stop flight scenarios
In the 1st
scenario, the "outsider" non-stop-flight, anything else (fares, frequent flier, brand, product,
etc…) assumed being equal, can get about 30% of market share to be compared to 13% only an
additional 1-stop, 11H25 trip time flight from Lyon would capture. Of course the non-stop service
is far more attractive than an additional 1-stop one, but cannot divert sufficient passenger demand –
18 daily passengers one way – to balance the cost of operating this flight even with the smaller
aircraft available on the market.
If the same flight is operated by an already existing airline/alliance (Skyteam in our example) the
new programme flight gains an additional 25% share of the market which is slightly less than 28%
of the outsider contender would get. This difference comes from the demand spill between the
different flights offered by an airline/alliance. Once again the passenger gain in this example – 12
additional daily passengers one way – is far too low to balance the cost of opening a non-stop
service.
This example reminds us how powerful the "frequency weapon" only large hubs can develop, is for
preventing new comers from entering the long haul market even with such an attractive product as
non-stop flights. Hubs can be viewed as fortresses of which the 1st
row of defense walls is
frequencies. It means that the market fragmentation some have been claiming as being the future of
network development for decades may prove to be not that profitable and economically sustainable
for airlines.
Shorter transfer times at hubs: a competitive asset?
It is common knowledge that the shorter the transfer times in a hub the better for passengers. This
assertion directly drives the number of waves in a hub and spoke system, yield discount to balance
the burden a longer trip time is for passengers, etc… The approach we have detailed in these pages
offer the ability to test this "well known" assumption. It will be based on a European-like example
of a short haul/long haul trip.
Let us suppose that we look at the traffic between two cities A and B with transfer in two hubs H1
and H2. The following chart summarizes the different set of assumptions our scenarios are based on:

15. Daniel SALLIER 15
Figure 10: General assumptions
This example could be seen as a "standard" route between a continental Western Europe destination
and an East-Cost North American one. In addition it is among the shortest long haul routes served
from Europe. The following chart represents H1 share of the market as a function of transfer time
difference between H1 and H2 transfer time.
Figure 11: Transfer time difference and market share

16. Daniel SALLIER 16
This chart makes explicit a rather low market share sensitivity to transfer time difference between
hubs: about 1H00 trip time difference does not make a real difference.
The consequences of this item left to be fully confirmed by a more exhaustive set of route analysis,
may seriously challenge what is today "well known" on trip time effects on demand:
1/ it could not pay off for an already efficient airport investing huge amount of money for
reducing by some quarters of hour its transfer time for its short haul/long haul connecting
passengers (short haul/short haul is another issue);
2/ it could mean that the short haul/long haul hub and spoke system enjoys a higher operational
flexibility than the one airlines and airports may consider dealing with;
3/ it could be a waist of airline income to discount ticket prices to balance the longer trip time
"burden" (up to a certain level);
4/ it could change the views of both the airlines and the aircraft manufacturers on the operational
cruise speed of their aircraft mostly in a time of pretty expensive fuel price;
5/ maybe the criteria of trip time to sort flights displayed by GDS system should be changed for a
more relevant one;
6/ it could raise up the issue of the actual demand 14H00 and more non-stop flights (A340-500
and 777-200ER) can divert from 1-stop operations lasting 2 up to 3 hours longer. To make a
long story short are those extra long range aircraft worth the extra cost of operating them?
6. DISCUSSION AND CONCLUSION
Here are the very preliminary outputs of an approach which will require additional work to be done.
For exactly the same reasons as for the previous version of this approach, a passenger survey should
be conducted in order to confirm the all set of model parameters such as attractive departure/arrival
index, etc… A sensitivity analysis of model parameters should be done as well. The previous
version of this approach on frequency attractiveness proved to be very robust so there are no
reasons why this one would not be, … but the work is left to be done.
This approach, alike its former version, does not take into consideration the desired return flight
availability which may affect the passenger outward flight selection and, doing so, the market
shares of the different contenders on a route. This is not a very demanding theoretical issue to
address. A passenger survey which would provide the distribution of the staying time at destination
would allow an easy solving of this question.
In our opinion this approach can prove quite useful for addressing a very large scope of air transport
issues where passenger demand and trip time are interacting, such as:
- non-stop flight demand versus hubed one;
- transfer time at hubs;

17. Daniel SALLIER 17
- hub and spoke operational flexibility;
- high speed train versus air service;
- operational and economical cruise speed;
- aircraft performances. Does an almost Mach 1 long haul aircraft can divert sufficient (high yield)
demand to balance the (over)cost of its operations and providing a competitive advantage for the
airlines? Is the cruise speed advantage of regional jets a real competitive asset on (very) short
haul destinations when compared with turboprops?
- etc…
They might be plenty of other air transport issues we have not yet identified at this stage!
7. REFERENCES
LEK Partnership Limited,"Airport Capacity Requirements in the Stockholm Region", (1998)
Gronau R., " The Value of Time in Passenger Transportation: The Demand for Air Travel", National Bureau
of Economic Research (New York) (1970)
Sallier D., "Détournement modal, Le Modèle LEK", internal memorandum, Aéroports de Paris (December
2005)
Sallier D., "Daily Demand Distribution and Flight Programme Attractiveness for the Passengers on a City
Pair", ATRS annual Symposium (Sydney) (2011)