Traffic Forecasts Fare Policy Yield Management

Traffic forecasts
Fare Policy
Yield Management
Michel LEBOEUF
SNCF, Director for Major Projects
Paris, France
1/100
Traffic forecasts
Fare policy
Yield management
2/100
You want to implement a High Speed Rail link between A and B.
What will the traffic volume be?
What revenues can be expected from the passengers?
3/100
The profitability and the public welfare utility of a project
are strongly linked to its ability
to attract new passengers
The traffic forecasts are essential
in assessing a project profitability
and its public welfare utility
.
One of the two major risks of a HSR project
is the commercial risk
Therefore, the traffic forecasts are
a key issue when dealing with the project financing
and risk allocation
4/100
Predicting traffic volumes and revenues involves modelling techniques
mainly based on statistical approaches.
There are numerous modelling techniques.
We have learnt that:
-very sophisticated models require a lot of accurate data
-data gathering is a long and very costly process
-complexity in a model is often a weakness
-traffic prediction should be comprehensible for non-specialists
5/100
Traffic forecasts by OD pairs
Origin
City
A
Destination
City
B
G
A
D
C
6/100
E
19 One way OD pairs
F
B
AD AE AF AB AG
CD CE CFCB CG
DE DF DB DG
EF EB EG
FB FG
Baseline Situation
Annual
traffic
volumes
from A to B
Reference situation for road
Project situation for road
Project situation for rail
Reference situation for air
road
Project situation for air
air
Reference situation for rail
rail
2000
7/100
2008
2015
2065
First choice
Travel
Public transportation
Bus
8/100
Plane
Train
No travel
Private car
Rationality
Time
Value of time
Money
9/100
Traffic Forecasts and Marketing for
High Speed Train Services
10$
20$
50$
10/100
%
Log-normal distribution
100% people
0 10
11/100
20
30
Value of time ($/hour)
Air/rail competition
100$
Origin
City
Destination
City
60$
12/100
1hour
3 hours
Air/rail competition
Generalized Cost (Air)= Price (Air)+ v*Travel Time (Air)
GCAir = 100$+ v*1hour
Origin
City
Destination
City
Generalized Cost (Rail)= Price (Rail)+ v*Travel Time (Rail)
GCRail = 60$+ v*3 hours
13/100
Air/rail competition
Generalized Cost (Air)
Price (Air)+ v*Travel Time (Air)
GCAir = 100$+ v*1hour
=
Generalized Cost (Rail)
Price (Rail)+ h*Travel Time (Rail)
GCRail = 60$+ v*3 hours
People who value time as 20$/hour
don’t care about the transport mode because:
100$ + 1*20$ = 60$ + 3*20$
People who value time as less than 20$/hour
prefer the train because:
100$ + 1*10$ > 60$ + 3*10$
14/100
Rail passengers
%
Air passengers
55%
45%
0 10
15/100
20
30
Value of time ($/hour)
Baseline Situation
Annual
traffic
volumes
from A to B
Reference situation for road
Project situation for road
Project situation for rail
Reference situation for air
road
Project situation for air
air
Reference situation for rail
rail
2000
16/100
2008
2015
2065
Rail passengers
%
Air passengers
55%
45%
0 10
20
30
Value of time ($/hour)
30%
70%
0 10
17/100
20
30
Value of time ($/hour)
Air/rail competition
100$
1hour
Origin
City
Destination
City
60$
18/100
70$
2 hours
3 hours
Air/rail competition
Generalized Cost (Air)
Price (Air)+ v*Travel Time (Air)
GCAir = 100$+ v*1hour
=
Generalized Cost (Rail)
Price (Rail)+ h*Travel Time (Rail)
GCRail = 70$+ v*2 hours
People who value time as 30$/hour
don’t care about the transport mode because:
100$ + 1*30$ = 70$ + 2*30$
19/100
Modal shift from air to rail
%
Rail passengers
30%
+
25%
=
55%
20/100
0 10
20
30
Value of time ($/hour)
Air passengers
70%
25%
=
45%
% Rail / Rail+Air
100
80
60
40
20
0
21/100
0,0
2,0
4,0
6,0
8,0
10,0
Hours
Market shares on the Rail+Air market
on the Madrid-Sevilla OD pair
120,00%
100,00%
80,00%
60,00%
40,00%
20,00%
0,00%
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
22/100
Air
76,30 79,30 48,40 18,40 20,00 18,80 18,40 18,50 18,80 17,10 16,60 15,80 15,90
Rail
23,70 20,70 51,60 81,60 80,00 81,20 81,60 81,50 81,20 82,90 83,40 84,20 84,10
% Rail / Rail+Air
100
90
80
60
40
35
20
0
0,0
2,0
4,0
3h
23/100
6,0
5h30
8,0
10,0
Hours
HS1.2
9.367
8.817
HS1.1
8.389
8.009
7.925
7.634
7.3337.239
7.225
6.860
6,338
5.189
EUROSTAR
annual traffic
In million
passengers
3.223
0.228
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
24/100
Number of Eurostar tickets
week by week
NUMBER OF EUROSTAR TRIPS SOLD
Daily rhythm
Without the HS1 1
With the HS1 1
Commissioning of the CTRL1
25/100
AIR LIBERTE
Evolution of air traffic volume
between
Paris and Marseilles
Mediterranean TGV
Commissioning
Id TGV
20,00%
month M -Y/month M (Y-1)
Evolution (%)
10,00%
0,00%
-10,00%
-20,00%
-30,00%
-40,00%
ma avr- mai- juin- juil- août- sept- oct- nov- déc- janv- févr- ma avr- mai- juin- juil- août- sept- oct- nov- décrs-01 01
01 01 01
01 01 01
01 01 02
02 rs-02 02
02 02 02
02 02 02
02
02
26/100
Gravity model & mobility
d
S
S
Mm
S= k
27/100
d2
u
M:Mass
m:mass
d:distance
S: strength
k: constant
Gravity model & mobility
Generalized travel cost (Gc)
City A
Traffic
Traffic
POP * pop
T= k
28/100
Gc2
City B
Gravity model & mobility
Gc = f (travel time,
access and egress times,
average price,
number of connections,
service frequency)
29/100
Gravity model & mobility
Generalized travel cost (Gcij)
City A
TA
B
30/100
=
K * PopEmA * PopAtt B
Gcγ A
B
T AB
City B
T
Traffic on the (ij) OD pair
K
Constant factor
Em Characteristic of the Emission zone
Att Characteristic of the Attraction zone
Gc Generalized cost of transport between zones i & j
Gravity model & mobility
Generalized travel cost
(Gcij)
City A
TA
B
+ΔT A
TA
B
31/100
=
K * PopEmA * PopAtt B
Gcγ A
B
City B
B
ΔT AB
T AB
=
-γ
ΔGc A
Gc A
B
B
Gravity model & mobility
TA
City A
B
+ΔT A
ΔT AB
T AB
32/100
=
-γ
City B
Where does
the traffic increase
come from?
B
ΔGc A
Gc A
B
B
Partly from road
33/100
and from
a mobility
increase
The best strategy against
competition by road is to increase
the service frequency.
High speed trains are apt
to be operated like shuttles.
34/100
 The time table is based on departures
every 4 to 12 minutes
between Tokyo and Omiya and Takasaki
from 6AM to 11 PM.
"ASAHI" (Tokyo - Niigata)
Takasaki
35/100
Omiya
 28 trains a day between
Paris and Brussels
a departure every 30 minutes
from 6h25 to 22h31
with a 1h25 trip duration
36/100
Conclusion on traffic forecasts
37/100
Forecast
Traffic volume multiplier
comparision
between
forecasts and actual figures
38/100
Modal split on the Madrid-Sevilla market
before the commissioning of the AVE
car
Conventional train
air
bus
15%
11%
60%
14%
Modal split on the Madrid-Sevilla market
after the commissioning of the AVE
The car has lost
more than 40%
of its market share
34%
Conventional train
air
52%
bus
8%
39/100
car
2%
4%
AVE
Air has lost
more than 60%
of its market share
Market shares in France
9%
On OD pairs
to and from Paris
TGV plays
a major role
54%
37%
TGV
Car
Air
1%
Nationwide,
the car is still
the dominant mode
37%
62%
40/100
Traffic forecasts
Fare policy
Yield management
41/100
The SNCF fare policy for
long distance passenger services
has been…
… strongly linked to the history
of the company
42/100
3 fare policies
have been successively
experienced, based on
43/100
1 – A fare policy based
on the production cost
1937:
Creation of SNCF
by socializing
the former private companies
44/100
Pf2 = P0 + d*p
Pf2 = full fare 2nd class
P0 = constant
d = distance (km)
p = unit price per km
45/100
Pf1 = 1.5* Pf2
Pf1 = full fare 1st class
Pf2 = full fare 2nd class
46/100
Customer ticket price:
Pc = Pf1 or f2*(1-discounting rate/100)
47/100
2 types of discounts:
- Social discounts
- Commercial discounts
48/100
2 main issues:
1 – Which part of the production cost
is fixed and which part is variable?
2 – What is the most relevant parameter
for the variable charges?
49/100
Main features of the price formula:
- Equality between all routes and regions
- Decreasing in relation to distance,
- The price level is based on the long term
economic viability of the rail sector,
50/100
2 – The transition to
a fare policy
based on the market
51/100
2 – The transition to
a fare policy
based on the market
1981/1983:
Commissioning
of the new
high speed line
from Paris to Lyon
52/100
Amsterdam
PROFIL DE LA LIGNE ANCIENNE PARIS-DIJON-LYON
Den Haag
altitude ( en m )
Rotterdam
500
London
Oostende
Calais Frethun
Antwerpen
Calais-V.Dunkerque
Liège
300
Namur
Valenciennes
200
TGV Haute-Picardie
Le Havre
100
Rouen
Metz
Champagne-Ardenne
0
Meuse
Paris
Strasbourg
Lorraine
Massy TGV
Brest
St Brieuc
Nancy
Marne-la-Vallée - Chessy
Melun
Rennes
Quimper
Lorient
Vannes
Colmar
Sens
Le Mans
Tours
200
300
400
distance ( en km )
Sélestat
Zürich
Dijon
Mouchard
Nantes
FuturoscopeArgenton/Creuse
Poitiers
Niort
Dole
Chalon
sur-S. Lons-le
-Saunier
Le Creusot TGV
Mâcon TGV
La Rochelle
Lyon
St-Étienne
Angoulême
Bern
Lausanne
Brig
Bourg
en-B. Genève
Lyon
Saint Exupéry
TGV
St-Gervais-les-Bains
Milano
altitude ( en m )
Modane
Bordeaux
Valence Ville
Libourne
Grenoble
Valence TGV
PROFIL DE LA LIGNE NOUVELLE PARIS-SUD-EST
Bourg-St-Maurice
Torino
500
Arcachon
Agen
Toulouse
Pau
Oloron
Tarbes
Buzy
Lourdes
à grande vitesse
à grande vitesse en construction
classiques empruntées
TGV
53/100
Carcassonne
Perpignan
300
200
Marseille
Toulon
100
0
Paris
Lignes
Lignes
Lignes
par les
Narbonne
Ventimiglia
SAÔNE
Bayonne
Hendaye
Nice
Avignon Centre
Nîmes
Avignon TGV
Montpellier
Arles
Aix-en-Provence
TGV
Béziers
100
200
300
distance ( en km )
400
Lyon-Part-Dieu
Dax
400
Orange
Montauban
Combs-la-Ville
Montélimar
500
512
Mulhouse
Montbéliard
Besançon
St-Pierre-des-Corps
Le Croisic
Belfort
Montbard
Vendôme TGV
Angers
La Baule
100
Paris
Lannion
Lyon-Perrache
Lens
Arras
400
Bruxelles
Lille
Boulogne
Köln
425
53
A Route 20%
shorter:
The issue raised
by the
new high speed
offer:
A much better
service
for a lower price?
54/100
Of doors and windows
in the fare policy
55/100
55
Benefits brought by
the seat booking obligation
for the customer:
- Better sizing of the production means,
- Service guarantee for the client,
- Better knowledge of the market,
- Possibility to adjust the fare
according to the departure hour.
56/100
On the Paris-Lyon route,
the TGV ticket
is proposed at the same price
as the ticket
with conventional trains
57/100
SNCF specifications document
(art 14):
1°) The price paid
by the customer
is determined by SNCF
according to
a general basic fare
to be applied
for a 2nd class passenger;
2°)The basic fare
will have to be agreed
by the State...
58/100
1983:
A new SNCF
and
a new transport
regulation
1993:
Commissioning
of the Northern TGV
59/100
Two improvements of the fare policy:
- Trains with extra charges
- Blue-white-red fare policy
60/100
The state government
agrees to abandon
the distance
based fare policy
and SNCF
moves towards
market prices
61/100
1994:
Change
in the SNCF
guidelines
3 – The client
risk sharing and loyalty
62/100
1994
1996:
SNCF
revamps
its
booking
system
63/100
… and starts
revenue management
The
Value
For
money
64/100
Revenue management techniques
are based on market segmentation and
the assumption
that all customers
do not appraise
the value of
a product or a service
at the same level
The value for money price level
makes the synthesis of the various
components and strengths
acting on the market
in real time
65/100
A new range of fares based on
-the client loyalty
-the service provided to the client
-the client flexibility
66/100
Generalization of market prices
Season
Tickets
(>-50%)
Frequent travellers
12/25 discount 3
Card (-50%)
Casual travellers
2
« Discovery » (-25%)
Road/air/non mobile
67/100
1
No anticipation
+discount
function of
The client
loyalty
Booking at D-120
No
possibility
68/100
Up to D-1
Up to H-1
From H+1
Up to D+60
Exchange lattitude
Amsterdam
Den Haag
Rotterdam
London
Oostende
Antwerpen
Calais-V.
Dunkerque
Bruxelles
Calais Frethun
Lille
Boulogne
Liège
Lens
Arras ValenciennesNamur
Charleville-Mézières
Le Havre
Granville
Rouen
Chars
Vernon
TGVReims
Haute-Picardie
Champagne-Ardenne
Metz
Meuse
Köln
Sarrebrücken
Strasbourg
Paris
Lorraine
Strasbourg
Toul
Troyes - ChessyNancy
Massy TGV
Marne-la-Vallée
St Brieuc
Melun
Sélestat
Mulhouse
Rennes
Orléans
Quimper
Colmar
Sens
Culmont( Les
Aubrais)
Lorient
Le Mans
Chalindrey Mulhouse
Vannes
Belfort
Montbard
Vendôme TGV
MontbéliardZürich
Angers
Tours
La Baule
Besançon
BourgesNevers
St-Pierre-des-Corps
Dijon
Le Croisic
Nantes
Dole Mouchard Bern
Chalon
Le Creusot TGV sur-S.Lons-le
Les Sables
Futuroscope
Argenton/Creuse
-d'Olonne
-SaunierLausanne
Brig
Évian
Poitiers
Bourg
Niort
Bourg
Genève
Mâcon TGV -en-B.
en-B.
St-Gervais
La Rochelle
St-Gervais-les-Bains
Lyon
Bourg-St-Maurice
Exupéry
Lyon Saint
Bourg-St-Maurice
TGV
Milano
St-Étienne
Angoulême
Modane
Grenoble
Torino
Bordeaux Libourne
Valence Ville Valence TGV
Arcachon
Montélimar
Agen
Orange
Montauban
Ventimiglia
Nîmes Avignon Centre Nice
Dax
Avignon TGV
Toulouse
Montpellier
Arles
Bayonne
Aix-en-Provence
Pau
Hendaye
Béziers
TGV
Tarbes
Oloron
Buzy
Marseille
Carcassonne Narbonne
Lourdes
Toulon
Lannion
Brest
Lignes
Lignes
Lignes
par les
à grande vitesse
à grande vitesse en construction
classiques empruntées
TGV
69/100
Perpignan
Traffic Forecasts for High Speed Train Services
Traffic Forecasts for High Speed Train Services
Best Travel Times
2003
2007 2010
2010 2015
2015
2020
Brest
4h01 3h31 3h00
Reims
Metz/Nancy
1h25 0h45
2h45/2h40 1h30
Strasbourg
PARIS
Quimper
4h13 3h43 3h00
3h50 2h20 2h00
1h00 2h00 3h00
4h00 5h00
Nantes
6h00
Mulhouse
2h01 1h53
4h31 3h10* 2h30
Bordeaux
2h57 2h25 2h09
Montpellier
Toulouse
5h07 4h42 4h10/ 3h00
70/100
3h15 3h00
Chambéry
Évian
2h40 2h10
4h35 4h05
Marseille
3h00
Nice
5h30 4h35 /3h25
Traffic Forecasts for High Speed Train Services
Traffic Forecasts for High Speed Train Services
Francfort
Amsterdam
4h10 3h10
Best Travel Times
Cologne
5h35 3h45 3h25
3h56 3h36 3h00
Stuttgart
Luxembourg
Londres
5h55 3h50 3h30
3h35 2h15
3h00 2h10
Munich
Bruxelles
8h20 6h00 5h40
1h25
2003
2007 2010
2010 2015
2015
2020
PARIS
1h00 2h00 3h00
4h00 5h00
6h00
Bâle
4h50 3h30 2h49
Genève
3h34 3h00
Turin
5h23 4h53 3h15
Milan
6h50 5h28 4h02
Madrid
13h00 8h15 8h00 7h25
Barcelone
8h25 5h50 5h35 5h00
sources : DGDC & TGV Est
71/100
180,0
160,0
150,0
140,0
160,9
159,0
157,0
154,5
151,6
148,0
145,0
142,0
137,7
Forecasted
132,0
128,8
traffic growth
124,6
with network extension
118,9
High speed traffic in France
(million passengers)
120,0
109,2
98,8100,8
95,4
92,191,0
87,5
83,4
77,7
72,0
100,0
80,0
65,7
57,6
60,0
49,3
46,7
41,742,6
39,3
40,0
31,8
21,9
18,019,5
16,7
16,6
14,6
20,0
7,3
1,5
0,0
72/100
9,9
Traffic forecasts
Fare policy
Yield management
73/100
Yield management
is the way you manage
train capacity and sales
in real time
74/100
Internet has completely changed
customer behaviour:
real time information
drives the market toward perfection
75/100
However train tickets are not
to be auctioned off
76/100
Railway yield management
is extremely complex:
much more comlex than
in the air industry.
77/100
There are roughly two situations
whether
the operator has or does not have
any constraints:
public (even part) ownership of infrastructure
will always lay constraints on any private operator
78/100
The low cost air companies case
somehow illustrates
the no-constraint model
79/100
Basic principle for a constraint-free model
Departure day
Number of reservations for a given train
Double
unit
train capacity
? (3)
Single
unit
train capacity
? (2)
? (1)
D-120
80/100
First reading day
D
Time (days)
Train
occupancy
G
A
100%
AD AE AF AB AG
CD CE CFCB CG
DE DF DB DG
EF EB EG
FB FG
1
2
3
4
8:00
81/100
E
C
50%
0%
D
5
6
Train number
Departure time
F
B
Basic principle for a constraint-free model
Forecasting
model
Departure day
Price per seat ($)
Double
unit
train capacity
Single
unit
train capacity
D-120
82/100
First rReading day
D
Competition ?
Real time
assessment
? (2)
of the appropriate fare level
to maximize
? (3)
revenues ? (1)
and/or occupancy
Time (days)
Basic principle for a constraint-free model
Single
unit
train capacity
D-120
83/100
D
Ramp up profiles
Double
unit
train capacity
Departure day
Number of reservations for a given train
Time (days)
Basic principle for a constraint-free model
Departure day
Number of reservations for a given train
Double
unit
train capacity
? (3)
Single
unit
train capacity
? (2)
? (1)
D-120
84/100
First reading day
Second reading day
D
Time (days)
Basic principle for a constraint-free model
Departure day
Price per seat ($)
Double
unit
train capacity
Single
unit
train capacity
?
D-120
85/100
First rReading day
Second reading day
D
Time (days)
Train
occupancy
100%
50%
0%
1
2
3
4
5
6
8:00
6:30
86/100
9:00
Train number
Departure time
Basic principle for a constrained model
Constraints may be laid on:
- Maximum fare
- Average fare
- 1st and 2nd class
- Social discounts
- Others (children groups, for example)
87/100
Basic principle for a constrained model
Train
occupancy
1st class
Service
100%
50%
2nd class
0%
1
2
3
4
5
6
8:00
6:30
88/100
9:00
Train number
Departure time
No anticipation
+discount
function of
the client
loyalty
Booking at D-120
No
possibility
89/100
Up to D-1
Up to H-1
From H+1
Up to D+60
Exchange latitude
Basic principle for a constrained model
Train
occupancy
Frequent
Travellers
1st class
Casual
Travellers
100%
Loyalty
Frequent
Travellers
50%
2nd class
0%
1
2
3
4
5
6
8:00
6:30
90/100
9:00
Train number
Departure time
Casual
Travellers
Basic principle for a constrained model
Train
occupancy
Frequent
Travellers
1st class
Casual
Travellers
100%
Refundable
Non
refundable
50%
2nd class
0%
1
2
3
4
5
6
8:00
6:30
91/100
9:00
Train number
Departure time
Casual
Travellers
Risk
Frequent
Travellers
Basic principle for a constrained model
Forecasting
model
Double
unit
train capacity
D-120
92/100
Real time
allocation of seats between
? (2)
classes of service
and loyalty/risk quotas
? (3)
so as to maximize
? (1)
the revenues or the occupancy
First rReading day
D
Competition ?
Single
unit
train capacity
Departure day
Price per seat ($)
Time (days)
Basic principle for both models:
Optimization of the pricing
so that
yield system will
maximize the revenues
or the traffic volume
93/100
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X*
FS
ETR 460/480
Premium 1on board the highETR
500
Services available
speed
trains
Catering at the seat
X*
Glass of welcome
X
Snack meal
Newspapers
X
Bar / Cafeteria / restaurant
X
Mobile grocery
Video
Audio (4 musical channels)
Games for children
Cloakroom
X
On board telephone
Parking (i)
Taxi booking
X
Access to club lobby
X1
Baby wraping facilities
Commodities for disabled people
Compartments dedicated to parents with children
Electric plugs
X
Mobile stations
Dedicated Business compartments
* Included in the 1st class ticket price
1 Free for SNCF "grand voayageurs"
2 in ICE 3
94/100
X
X
X
X
X
X
X*
X*
X*
X
X*
X*
X*
X
ETR 450
X*
X*
X*
X
X
X
X2
X
X
X*
X
X
X
X
X
X
X
X
X
X
X
X
X*
X*
X
X
X
X
X
X
X
X
X
X
165 €
97 €
39 €
25 €
Easyjet
Prem ’s
25 €
TGV
Comparison of the width of the scale of fares
of Easyjet and TGV
on the Paris-Geneva route
95/100
Price ranges for 2-hour high speed trip duration
Rail
Market share
above 90%
Rail
Market share
above 85%
96/100
Price ranges
€
160
140
140
120
114
110
100
93
108
1ère Classe PT +
95
92
90
80
Prem's
60
2de classe TR maxi
40
20
35
34
26
22
36
26
0
Paris Lyon
Würzburg
Hanovre
97/100
1ère Classe PT
Stuttgart
Köln
Madrid
Séville
Madrid
Barcelona
Conclusion
98/100
Traffic
forecasts
99/100
Pricing
Yield
management
Thank you for your attention!
100/100