Traffic Forecasts - Fare Policy Yield Management
Transcription
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 be the traffic volume? 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. Modelling techniques are many. We have learnt that: -very sophisticated models require a lot of acurate 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 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 6/100 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 2008 2015 2065 7/100 First choice Travel Public transport Bus Plane Train No travel Private car Rationality 8/100 Time Value of time Money 9/100 Traffic Forecasts and Marketing for High Speed Train Services $10 $20 $50 10/100 % Log-normal distribution 100% people 0 10 20 30 Value of time ($/hour) 11/100 Air/rail competition $100 Origin City 1hour Destination City $60 3 hours 12/100 Air/rail competition Generalised Cost (Air)= Price (Air)+ v*Travel Time (Air) GCAir = $100+ v*1hour Origin City Destination City Generalised Cost (Rail)= Price (Rail)+ v*Travel Time (Rail) GCRail = $60+ v*3 hours 13/100 Air/rail competition Generalised Cost (Air) Price (Air)+ v*Travel Time (Air) GCAir = $100+ v*1hour = Generalised Cost (Rail) Price (Rail)+ h*Travel Time (Rail) GCRail = $60+ v*3 hours People whose value of time is $20/hour don’t care about the transport mode because: $100 + 1* $20 = $60 + 3* $20 People whose value of time is less than $20/hour prefer the train because: $100 + 1* $10 > $60 + 3* $10 14/100 Train passengers % Air passengers 55% 45% 0 10 20 30 Value of time ($/hour) 15/100 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 2008 2015 2065 16/100 Train passengers % Air passengers 55% 45% 0 10 20 30 Value of time ($/hour) 30% 70% 0 10 20 30 Value of time ($/hour) 17/100 Air/rail competition $100 1hour Origin City Destination City 60$ $70 2 hours 3 hours 18/100 Air/rail competition Generalised Cost (Air) Price (Air)+ v*Travel Time (Air) GCAir = $100+ v*1hour = Generalised Cost (Rail) Price (Rail)+ h*Travel Time (Rail) GCRail = $70+ v*2 hours People whose value of time is $30/hour don’t care about the transport mode because: $100 + 1*$30 = $70 + 2*$30 19/100 Modal shift from air to rail % Train passengers 30% + 25% = 55% 0 10 20 30 Value of time ($/hour) Air passengers 70% 25% = 45% 20/100 % Rail / Rail+Air 100 80 60 40 20 0 0,0 2,0 4,0 6,0 8,0 10,0 Hours 21/100 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 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 22/100 % Rail / Rail+Air 100 90 80 60 40 35 20 0 0,0 2,0 4,0 3h 6,0 5h30 8,0 10,0 Hours 23/100 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 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 d2 u M:Mass m:mass d:distance S: strength k: constant 27/100 Gravity model & mobility Generalized travel cost (Gc) City A Traffic Traffic City B POP * pop T= k Gc2 28/100 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 = 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 30/100 Gravity model & mobility Generalized travel cost (Gcij) City A TA B = K * PopEmA * PopAtt B Gcγ A B TA B +ΔT A City B B ΔT AB T AB = -γ ΔGc A Gc A B B 31/100 Gravity model & mobility City A ΔT AB T AB TA B +ΔT A = -γ City B B ΔGc A Gc A B Where does the traffic increase come from? B 32/100 Partly from road and from a mobility increase 33/100 The best strategy against road is to increase the service frequency. High speed trains are apt to be operated like shuttles. 34/100 Î 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 Omiya 35/100 Î 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 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% car Conventional train air 52% bus 8% 2% 4% The air has lost more than 60% of its market share AVE 39/100 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 passengers 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 discount: - 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 - Degressivity to distance, - The level of price 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 Antwerpen Calais-V.Dunkerque Lille Liège 300 Namur Valenciennes 200 TGV Haute-Picardie Le Havre 100 Rouen Metz Champagne-Ardenne Strasbourg Lorraine Massy TGV Brest St Brieuc Nancy Marne-la-Vallée - Chessy Melun Rennes Quimper Lorient Vannes Colmar Sens Le Mans Tours 200 300 distance ( en km ) 400 500 512 Sélestat Mulhouse Montbéliard Besançon St-Pierre-des-Corps Le Croisic Belfort Montbard Vendôme TGV Angers La Baule 100 Paris 0 Meuse Paris Lannion Lyon-Perrache Lens Arras 400 Bruxelles Calais Frethun Boulogne Köln Zürich Dijon Mouchard Nantes Dole Chalon sur-S. Lons-le -Saunier Le Creusot TGV Futuroscope Poitiers Argenton/Creuse Niort 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 300 200 Marseille Buzy Lourdes Carcassonne Perpignan Narbonne Toulon 100 0 Paris Lignes à grande vitesse Lignes à grande vitesse en construction Lignes classiques empruntées par les TGV 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 425 53 53/100 A 20% shorter Route: 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 55/100 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... 1983: A new SNCF and a new transport regulation 58/100 1993: Commisioning 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 upon the abandonment of the distance based fare policy and SNCF moves towards market prices 1994: Change in the SNCF guidelines 61/100 3 – The client risk sharing and loyalty 62/100 1994 1996: SNCF revamps its booking system … and starts revenue management 63/100 The Value For money Revenue management techniques are based on market segmentation and the assumption that all customers do not appraise at the same level the value of a product or a service 64/100 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 1 67/100 No anticipation +discount function of The client loyalty Booking at D-120 No possibility Up to D-1 Up to H-1 From H+1 Up to D+60 Exchange lattitude 68/100 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 TGV Reims Haute-Picardie Köln Sarrebrücken Champagne-Ardenne Metz Meuse Strasbourg Paris Lorraine Strasbourg Toul Troyes - ChessyNancy Massy TGV Marne-la-Vallée St Brieuc Melun Sélestat Mulhouse Rennes Colmar Orléans Quimper Sens Culmont( Les Aubrais) Lorient Le Mans Chalindrey Mulhouse Vannes Belfort Montbard Vendôme TGV MontbéliardZürich Angers Tours Besançon La Baule BourgesNevers St-Pierre-des-Corps Dijon Le Croisic Nantes Dole Mouchard Bern Chalon Le Creusot TGV sur-S.Lons-le Les Sables Futuroscope -d'Olonne -SaunierLausanne Brig Évian Poitiers Bourg Niort Bourg Mâcon TGV -en-B. en-B. Genève St-Gervais La Rochelle St-Gervais-les-Bains Bourg-St-Maurice Lyon 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 Marseille Carcassonne Narbonne Lourdes Toulon Lannion Brest Argenton/Creuse Oloron Buzy Lignes à grande vitesse Lignes à grande vitesse en construction Lignes classiques empruntées par les TGV Perpignan 69/100 Best Travel Times 2003 2007 2010 2015 2010 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 3h15 3h00 Chambéry Évian 2h40 2h10 4h35 4h05 Marseille 3h00 Nice 5h30 4h35 /3h25 70/100 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 2015 2010 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 71/100 sources : DGDC & TGV Est 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) 120,0 109,2 100,0 87,5 83,4 77,7 72,0 80,0 98,8100,8 95,4 92,191,0 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 9,9 1,5 0,0 72/100 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 fully changed customers’ 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 complex than in the air industry. 77/100 There is roughly two situations whether or not the operator has any constraints: public (even partly) 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 First reading day D Time (days) 80/100 Train occupancy G A 100% E F B C AD AE AF AB AG CD CE CFCB CG DE DF DB DG EF EB EG FB FG 50% 0% D 1 2 3 4 5 6 Train number Departure time 8:00 81/100 Basic principle for a constraint-free model Forecasting model Departure day Price per seat ($) Double unit train capacity Single unit train capacity D-120 First rReading day D Competition ? Real time Assessment ? (2) of the appropriate fare level so as to maximize ? (3) the revenues? (1) and/or the occupancy Time (days) 82/100 Basic principle for a constraint-free model Single unit train capacity D-120 D Ramp up profiles Double unit train capacity Departure day Number of reservations for a given train Time (days) 83/100 Basic principle for a constraint-free model Departure day Number of reservations for a given train Double unit train capacity ? (3) Single unit train capacity ? (2) ? (1) D-120 First reading day Second reading day D Time (days) 84/100 Basic principle for a constraint-free model Departure day Price per seat ($) Double unit train capacity Single unit train capacity ? D-120 First rReading day Second reading day D Time (days) 85/100 Train occupancy 100% 50% 0% 1 2 3 4 5 6 Train number Departure time 8:00 6:30 9:00 86/100 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 Train number Departure time 8:00 6:30 9:00 88/100 No anticipation +discount function of The client loyalty Booking at D-120 No possibility Up to D-1 Up to H-1 From H+1 Up to D+60 Exchange lattitude 89/100 Basic principle for a constrained model Train occupancy 1st class Frequent Travellers Casual Travellers 100% Loyalty Frequent Travellers 50% 2nd class 0% 1 2 3 4 5 6 Casual Travellers Train number Departure time 8:00 6:30 9:00 90/100 Basic principle for a constrained model Train occupancy 1st class Frequent Travellers Casual Travellers 100% Refundable Non refundable 50% 2nd class 0% 1 2 3 4 5 6 Risk Frequent Travellers Casual Travellers Train number Departure time 8:00 6:30 9:00 91/100 Basic principle for a constrained model Forecasting model Double unit train capacity D-120 Real time apportionment 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) 92/100 Basic principle for both models: Optimization of the pricing so that Yield system will maximize the revenues or the traffic volume 93/100 Services available on board the high speed trains Services proposés SNCF Premium X* X Catering at the seat Glass of welcome 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 childr 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 ère 1 DB classe X X X RENFE Club Preferente X X X* X* X X X X ETR 500 FS ETR 460/480 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 94/100 165 € 97 € 39 € 25 € Easyjet Prem ’s 25 € TGV Comparison of the width of the scale of fares of Easyjet and TGV on the Paris-Gineva 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 1ère Classe PT 35 34 26 22 36 26 0 Paris Lyon Würzburg Hanovre Stuttgart Köln Madrid Séville Madrid Barcelona 97/100 Conclusion 98/100 Traffic forecasts Pricing Yield management 99/100 Thank you for your attention! 100/100