A DSS Reshapes Revenue Management in Railway Networks Ting Li Department of Decision and Information Sciences Rotterdam School of Management, Erasmus University Pre-ICIS SIG-DSS Workshop 2006 December 10, 2006, Milwaukee, Wisconsin, USA

Outline Research background and questions Research studies and methodology –Impact of smart card adoption on RM -- multiple case study –Customer behavioral responses to differentiated pricing -- stated preference experiment (SP) –RM DSS -- simulation Future work and discussion Outline

Motivation  Business needs Diffuse the concentration of peak load Increase capacity utilization  Advancement of ICT Problem: information and decision imbalancing, lack of reservation system / booking data Smart card adoption makes it possible  Increased application of Revenue Management “Selling the right capacity to the right type of customers at the right time for the right price as to maximize revenue.” Great success: American Airlines ($500 million/y), National Car Rental ($56 million/y)  Privatization of Public Transport Motivation 0 24 Passenger Demand Vehicle Supply Over-capacity Missed-income

Research Questions  Research Objective Assess the possibilities of revenue management in contribution of customer data provided by a nation-wide smart card adoption in the Netherlands  Research Questions What type of differentiated pricing fare scheme is sensible & feasible? How customers respond to various forms of differentiated pricing? What are the impacts to the transportation network yield?  Research Approach Develop a Revenue Management Decision Support System (RM-DSS) prototype for Public Transport Operators

Previous Research  Information system research Dynamic pricing benefits consumers (Bakos, 1997). RM increases performance enterprises (increased customer information)  Revenue management literature Increased dynamic pricing strategies due to (Elmaghraby et.al., 2003) Increased availability of demand data Ease of changing prices due to new technologies Availability of decision support tools for analyzing demand Conditions: Perishable inventory, relatively fixed capacity, ability to segment market, fluctuating demand, high production cost and low marginal cost, flexible pricing structure and ICT capability

RM DSS Revenue Management DSS

World-wide Smart Card Implementation YearCity (Country)Transportation (Issuing Authority)Name of SC 1997Hong Kong (China)Octopus Cards LimitedOctopus* 1997Tampere (Finland)Tampere City TransportTampere Travel Card 1999Washington D.C. (U.S.A.) Washington Metropolitan Area Transit Authority SmarTrip 2000Taipei (Taiwan)Taipei Smart Card CorporationEasyCard 2001Warsaw (Poland)Warsaw Transport AuthorityWarsaw City Card 2001Tokyo (Japan)East Japan Railway Company (JR East)SUICA* 2001Paris (France)Régie Autonome des Transports Parisiens (RATP) Navigo Card 2002SingaporeEZ-Link Private LimitedEz-link* 2002Chicago (U.S.A.)Chicago Transit Authority (CTA)Chicago Card* 2003London (U.K.)Transport for London (TfL)Oyster* 2004Seoul (South Korea) Korea Smart Card Co., LtdT-Money 2006Beijing (China)Beijing Municipal Administration & Communications Card Company Limited Yikatong* 2006The NetherlandsTrans Link Systems (TLS)OV-chipcard* 2007 (planned) Toronto (Canada)The Greater Toronto Transportation Authority GTA Card World-wide Smart Card Implementation

Differentiated Pricing Strategy Uniform pricing vs. Dynamic pricing Customer-oriented pricing (direct-segmentation) Profile-based pricing (e.g. 65+, student) Usage-based pricing (e.g. bundle) Journey-oriented pricing (indirect-segmentation) Time-based pricing (time-of-day, day-of-week) Route / region-based pricing Origin-destination based pricing Mode-based pricing (e.g., transfer, P&R)

Framework Public Transport Operators’ rational Effects to Customers Data / information sources needed Fare media (Potential ICT) Framework RM DSS

Behavior Responses to Differentiated Pricing +30% 16:0018:00 Differentiated Price Traveler Frequent Traveler Infrequent Traveler Single / Return Ticket Reduction Card Season Card Reduction Card Differentiated price: 30% higher between 16:00-18:00 than off-peak price How do customers respond to it? Departure time change ( 18:00) Mode change (alternative: car) No change

Stated Preference Experiment Focus group interview Quantitative survey Stated preference experiment June and July ,000 invitations to panel members 4571 responses received (35% response rate) Each respondent is presented with 8 choice sets Each choice set contains two alternative products: one more expensive with less restrictions & less expensive with more restrictions.

Estimation Results RM DSS

Modeling of Demand Model of demand is the key … rather than asking “how much demand should we accept/ reject for each product” as airlines used to do, it is now natural to ask “which alternatives should we make available to our customers in order to profitably influence their choices” -- van Ryzin (2005) Computer simulation is an often-used methodology to study travel behavior as a cost effective alternative to field studies. Solving consumer optimization problems analytically are beyond computational ability Benefits concerning the magnitude of the price differences Multi agent micro-simulation

Modeling of Travel Behavior Characteristics Age Income Education Car ownership Past Experience Comfort Crowdness Punctuality Decision Window Departure time Schedule Tolerance Max. WTP Influenced by Travel purpose Income Activity Schedule Location Duration Timing Purpose Possible SchedulePassenger DisutilityProduct and Ticket Passenger Decision Departure time Mode Route Fare Passenger Disposition Passenger Choice Set Passenger Choice

Passenger Railway Networks Simulation Infrastructure Network Train Scheduling Capacity Supply Simulation Passenger Disposition Passenger Choice Set Passenger Decision Demand Simulation Dynamic Pricing Strategy => Evaluate dynamic pricing strategies on the transportation network yield Performance Metrics RM DSS CategoryMetrics Supply (train operation) Network capacity utilization (load factor) Spread in train loading (passenger distribution) Load factor (Peak and average load) Cost (per day per train) Demand (passenger travel) Passenger (#) Journey (number of trips) Revenue (Euro) Volume (Passenger*km)

Conclusion and Future work Conclusion and Future Work Understand customer behavior is the key What they say is what they will do? RM DSS Framework “Big brother” issue Sensitivity analysis Case study: High Speed Train (A’dam-Brussels-Paris)