A DSS Reshapes Revenue Management in Railway Networks

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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
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Outline
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

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Motivation
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

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Research Questions
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

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Previous Research
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

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RM DSS
Revenue Management DSS
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World-wide Smart Card Implementation
World-wide Smart Card Implementation
Year City (Country) Transportation (Issuing Authority) Name of SC
1997 Hong Kong (China) Octopus Cards Limited Octopus
1997 Tampere (Finland) Tampere City Transport Tampere Travel Card
1999 Washington D.C. (U.S.A.) Washington Metropolitan Area Transit Authority SmarTrip
2000 Taipei (Taiwan) Taipei Smart Card Corporation EasyCard
2001 Warsaw (Poland) Warsaw Transport Authority Warsaw City Card
2001 Tokyo (Japan) East Japan Railway Company (JR East) SUICA
2001 Paris (France) Régie Autonome des Transports Parisiens (RATP) Navigo Card
2002 Singapore EZ-Link Private Limited Ez-link
2002 Chicago (U.S.A.) Chicago Transit Authority (CTA) Chicago Card
2003 London (U.K.) Transport for London (TfL) Oyster
2004 Seoul (South Korea) Korea Smart Card Co., Ltd T-Money
2006 Beijing (China) Beijing Municipal Administration Communications Card Company Limited Yikatong
2006 The Netherlands Trans Link Systems (TLS) OV-chipcard
2007 (planned) Toronto (Canada) The Greater Toronto Transportation Authority GTA Card
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Differentiated Pricing Strategy
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, PR)

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Framework
Framework

• Public Transport Operators rational
• Effects to Customers
• Data / information sources needed
• Fare media (Potential ICT)

RM DSS
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Behavior Responses to Differentiated Pricing
Behavior Responses to Differentiated Pricing
Differentiated price 30 higher between
1600-1800 than off-peak price

• How do customers respond to it?
• Departure time change (lt1600 or gt1800)
• Mode change (alternative car)
• No change

Traveler
Infrequent Traveler
Frequent Traveler
Single / Return Ticket
Reduction Card
Season Card
Reduction Card
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Stated Preference Experiment
Stated Preference Experiment

• Focus group interview
• Quantitative survey
• Stated preference experiment
• June and July 2006
• 13,000 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.

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Estimation Results
Estimation Results
RM DSS
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Modeling of Demand
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

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Modeling of Travel Behavior
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RM DSS
Passenger Railway Networks Simulation
Category Metrics
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 (Passengerkm)

• gt Evaluate dynamic pricing strategies on the
  transportation network yield

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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 (Adam-Brussels-Paris
  )