A DSS Reshapes Revenue Management in Railway Networks

Ting LiDepartment of Decision and Information Sciences

Rotterdam School of Management, Erasmus University

Pre-ICIS SIG-DSS Workshop 2006December 10, 2006, Milwaukee, Wisconsin, USA

Ou

tlin

e

• 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

Moti

vati

on

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

Researc

h Q

uesti

on

s

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

Pre

vio

us R

esearc

h

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 DSSDiagnosis

Demand / Supply

PublicTransport

System

Smart CardData

Infrastructure /Vehicle Data

Extrapolatedemand

Travel behavior

ExtrapolateSupply

Information

Choosepricing strategy

Simulatedemand / supply

Evaluateperformance

activitiessystem Information input-output

Legend:

SelectedPricing Strategy

YieldReport

RM

DS

S

Revenue Management DSS

Worl

d-w

ide S

mart

Card

Im

ple

men

tati

on

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

World-wide Smart Card Implementation

Diff

ere

nti

ate

d P

ricin

g S

trate

gy

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)

Fra

mew

ork

• Public Transport Operators’ rational

• Effects to Customers

• Data / information sources needed

• Fare media (Potential ICT)

Framework

RM DSS

Beh

avio

r R

esp

on

ses t

o D

iffere

nti

ate

d P

ricin

g

Behavior Responses to Differentiated Pricing

+30%

16:00 18:00

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 (<16:00 or >18:00)

•Mode change (alternative: car)

•No change

Sta

ted

Pre

fere

nce E

xp

eri

men

t

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.

Esti

mati

on

Resu

lts

Estimation Results

RM DSS

Mod

elin

g o

f D

em

an

d

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

Mod

elin

g o

f Tra

vel B

eh

avio

r

Characteristics•Age•Income•Education•Car ownership

Past Experience•Comfort•Crowdness•Punctuality

Decision Window•Departure time•Schedule Tolerance

Max. WTPInfluenced by •Travel purpose•Income

Activity Schedule•Location•Duration•Timing•Purpose

Possible Schedule Passenger Disutility Product 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

DS

S

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 (Passenger*km)

Con

clu

sio

n a

nd

Fu

ture

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)