A DSS Reshapes Revenue Management in Railway Networks

1. 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

2. 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

3. Missed-income Passenger Demand Vehicle Supply Over-capacity 0 24 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

4. 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 Research Questions

5. 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 Previous Research

6. Revenue Management DSS RM DSS

7. World-wide Smart Card Implementation World-wide Smart Card Implementation

8. 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) Differentiated Pricing Strategy

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

10. +30% Differentiated Price 16:00 18:00 Behavior Responses to Differentiated Pricing Differentiated price: 30% higher between 16:00-18:00 than off-peak price • How do customers respond to it? • Departure time change (<16:00 or >18:00) • Mode change (alternative: car) • No change Behavior Responses to Differentiated Pricing Traveler Infrequent Traveler Frequent Traveler Single / Return Ticket Season Card Reduction Card Reduction Card

11. 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. Stated Preference Experiment

12. Estimation Results Estimation Results RM DSS

13. 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 Demand

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

15. Passenger Disposition Infrastructure Network Dynamic Pricing Strategy Performance Metrics Passenger Choice Set Train Scheduling Passenger Decision Capacity Supply Simulation Demand Simulation Passenger Railway Networks Simulation => Evaluate dynamic pricing strategies on the transportation network yield RM DSS

16. 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) Conclusion and Future work