A Passenger Revenue Management System (RMS)

for a National Railway in an Emerging Asian Economy (NREAE)

Goutam Dutta1

Priyanko Ghosh1

1Indian Institute of ManagementAhmedabad-380015

India

Outline

• Introduction and Motivation • Literature Search• Current Reservation System in NREAE• Optimization Model• Simulation of Passenger Demand• Forecasting Module• Expected Marginal Seat Revenue Approach• Recommendations

Full paper on this research is available from this journal.

A passenger revenue management system (RMS) for a National Railway in an Emerging Asian Economy

Goutam Dutta, Priyanko Ghosh

Journal of Revenue and Pricing Management 11, 487-499 (6 April 2012) doi:10.1057/rpm.2012.10 Research

Researchers not able to get a copy of this paper may directly contact the first author at goutam@iimahd.ernet.in

Introduction and Motivation

• Revenue Management System

• Work done by SNCF

• Several steps taken by NREAE

• A meeting with board member

• Visits by Officials of NREAE

Introduction to NREAE

1. One of the five largest in the world2. Runs 14000 trains daily with 9000 passenger

trains3. 30 million passengers travel daily in 7083

stations 4. Revenue about 19 billion USD

Challenges of NREAE

• The passenger segment is facing challenges from low fare airlines which promise customer satisfaction and less travel time

• The freight sector is facing challenges from trucks and other road carriers

Industries where Revenue Management applies

• Perishable Services (Products)

• Identification of Market Segmentation possible

• Demand is Uncertain and Fluctuating

• Fixed Capacity

• High Fixed Cost

• Low Marginal (Variable) Cost

• Advanced Reservation Possible

7

Revenue Management System

Current Data Historical Data

Forecaster

Optimizer

System Recommendations

Performance Monitoring and Reports

Revenue Management in Railways

• As per Talluri and Van Ryzin, (2004) following railways are using RM

• AMTRACK

• SNCF

• Eurostar

• VIA Rail Canada

Literature Search

Literature Search

• There are about 25 papers on applications of OR/MS in railroad

• These papers deal various topic related to IE/OR/MS and not in revenue management

• A few applications in railways revenue management

Literature Search

• Williamson (1992) formulates two mathematical programming models for network revenue management (stochastic and deterministic) but finds no significant difference between them

• Ciancimino et al. (1999) formulate a deterministic linear programming model and a probabilistic non-linear programming model for railways and show that the probabilistic model generates more revenue than the deterministic model

• Boyar (1999) analyzes the seat reservation problem by considering two scenarios – the price is the same for all tickets and it is proportional to the distance - and solves the problem by considering deterministic and concrete algorithms

• Bharill et al. (2008) apply revenue management principles on one of the trains of Indian Railways. They suggest a differential pricing strategy on the basis of passenger demand estimates to increase railway revenue

Comparison of ImplementationsObjective Impact

SNCF To face the competition from European railways and airlines they developed well designed decision support systems to attract passengers

- Revenue increased by 10 million francs

- Costs decreased by 3%

Canadian Railways To create a scheduling system for railways

- Cost reduced by $300 million- Total savings exceeded half a

billion dollars

Netherlands Railways To develop a new timetable that helps to operate a higher number of trains and causes fewer train delays

Passenger traffic increased by 15%- 87% of the trains reach their

destinations within 3 minutes of their scheduled time

- Annual profit is around E10 million in 2007

German Railways Increase revenues and reduce costs through better capacity utilization

- Around 1.5% revenue increased- Relief trains are 30% lower in

2003 than in 2002- Standing minutes are reduced by 16% in 2003 than in 2002

Revenue Management System

Current Data Historical Data

Forecaster

Optimizer

System Recommendations

Performance Monitoring and Reports

Various Fare Classes of NREAE Codes Extensions

1A First class air-conditioned

2A Air-conditioned 2-tier sleeper

FC First class

3A Air-conditioned 3-tier sleeper

CC Air-conditioned chair-car - only sitting accommodation (individual chairs) is provided

EC Executive class, or First class air-conditioned chair-car - only sitting accommodation (individual chairs) is provided

SL Sleeper class

2S Second class sitting - only sitting accommodation with bench style seats

Current Reservation System

• NREAE has two types of reservation systems – 1. PRS (Passenger Reservation System) and 2. UTS (Unreserved Ticketing System)

• 85% of tickets are booked by the PRS and 15% of tickets are booked by the UTS

• The revenue of NREAE is approximately Rs 931.59 billion (19.13 billion USD) from which one third is earned from passenger coaches and two thirds from freight

• The Passenger Reservation System (PRS) offers reserved seats to passengers in any train from any counter of the country

Current Reservation System• Advance booking starts 60 days prior to the day of

departure for all fare classes and for all trains

• The advance reservations are made on FCFS (First Come First Serve) basis

• NREAE has introduced a booking system called Tatkal (urgency based scheme) where one can book tickets two days in advance of the day of departure by paying an extra charge

• A passenger who books ticket in Tatkal, has to pay the total fare from origin to destination and as Tatkal quotas are usually filled up Tatkal earning is constant

Railways booking centers offer seats to Passengers in FCFS basis

Availability of seat Non availability of seat

Passenger asks for Tatkal (Urgent) quota (2 days prior of the journey date)

Confirmed tickets are issued on a regular basis

Tickets are not confirmed but overbooked in Reservation Against Cancellations (RAC) and Waiting List (WL) format

RAC ticket holders can board a reserved coach but are only assured sitting accommodation even if there are no cancellations.WL ticket holders are not even guaranteed such sitting accommodations and are entirely unconfirmed at the time of booking.

Cancellations occur and RAC and WL ticket holders get converted to confirmed tickets subject to the order of booking.

Availability of seat Non availability of seat

Confirmed tickets are issued on Tatkal

Booking Process

Revenue Management System

Current Data Historical Data

Forecaster

Optimizer

System Recommendations

Performance Monitoring and Reports

Optimization Model INDICES

• i: Origin indexed by i • j: Destination indexed by j• k: Fare class indexed by k• t: Time period indexed by t

SETS

• S: Set of all stations (1,2,3, ……..n)• L: Links {(i,j) i S, j S, i<j} for all the origin destination

pair• K: Set of fare classes (k=1,2,3,…….p)• T: Set of time period (t=1,2,3,…….q)

PARAMETERS

= Revenue for fare class k K for leg (i,j) L and for time period t T

= Expected Cancellations for fare class k K for leg (i,j) L and for time period t T

= Expected Demand forecasted for fare class k K for leg (i,j) L and for time period t T

= Total Capacity of the Train for time period t T

= Non Tatkal booking allowed for fare class k K and for time period t T

= Tatkal booking allowed for fare class k K and for time period t T

= Cancellation charges for fare class k K

VARIABLES = Number of tickets to be allocated for fare class k K and leg (i, j) L and for time period t T

= Boolean variable for fare class k K and seat number l and leg (i,j) L for time period t T

=1 if a seat number l is utilized for fare class k K , leg (i,j) L and for time period t T = 0 otherwise

tijk

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tCT

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Objective Function

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• First part is the revenue earning from passenger allocations• Second part is the revenue earned from cancellations that

is a constant term• Third part is the revenue earnings from Tatkal that is a

constant term

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Subject to :

Total Capacity Constraint

Non Tatkal booking is less than the difference between Total capacity and Maximum Tatkal booking allowed

<= - for all k K and all t T

Demand Constraint

Allocated seats should not exceed Expected Passenger Demand

<= for all i,j L , k K and for all t T

p

ktk

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Capacity Constraint:

<= for all i,j L, k K, (w=1,2….n-1) , and

for all t T

Stations: ● ● ● ● ● ………….● ● 1 2 3 4 5 n-1 n

Xij = passenger boarding from source station i to destination station j.

Capacity Configuration:

Station 1:X12 + X13 + X14 + X15……+X1(n-1)+ X1n <= Ck

Station 2:X23 + X24 +X25 +......+ X2(n-1) + X2n+ X13 + X14 + X15 +…….+X1(n-1) +X1n <= Ck

Station 3:X34 + X35 +......+ X3(n-1) + X3n + X14 + X15 +……+.X1(n-1) +.X1n + X24 + X25 +......+ X2(n-1) + X2n <= Ck

……..……..Station n:X1n + X2n + X3n + ……+X(n-1)n <= Ck

n

wjtijk

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i 11tk

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A seat can be utilized maximum 7 times

– Boolean variable =1 if a seat number l is utilized for leg (i,j) L ,fare class k K, and for time period t T

=0 otherwise

<= 7 for all i,j L , k K ,(w=1,2….n-1) , and t T = for all i,j L ,k K and for all t T

Non Negativity Constraint: >= 0 for all i,j L , k K and for all t T

tijkl

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• We collect data of train no.2901 which runs from a metro to a mini metro over the year 2008

• We consider maximum passenger allocations for an origin destination pair as forecasted demand data

• We solve the model in AMPL

(A Mathematical Programming Language) and CPLEX solver version 11.2

• The model was run for four fare classes, 14 stations and one day, in AMPL/CPLEX 11.2

• The adjusted problem deals with 54694 variables (54528 binary and 166 linear) and 15828 linear constraints (462750

non-zeros) and 1 linear objective (116 non-zeros)

Optimization Model

• The average optimal daily revenue comes to around Rs 509272

• We consider it as base stage and increase the passenger demand by 10% in five stages

• In each stage revenue is increased• Optimal revenue depends on passenger demand• So accuracy of forecasting of passenger demand

plays a crucial role in optimization model

Optimization Model

Revenue Management System

Current Data Historical Data

Forecaster

Optimizer

System Recommendations

Performance Monitoring and Reports

Simulation of Passenger Demand

• Uncertainty is a crucial feature of passenger demand • We conduct a simulation study to capture this stochastic

nature of demand• Passenger demand follows normal distribution (p-value of

KS statistic is <0.01)• For one year period we compute the mean and standard

deviation of passenger demand of origin destination and use as inputs in simulation

• We simulate passenger demand for 100 times for each fare class and for origin destination and build the demand matrix

• We refer this as stage 0 or the base stage

• We run our optimization model with these demand matrices for 100 times and compute the optimal revenue

• We increase the mean and standard deviation by 10% in each stage and simulate passenger arrivals for each fare class and for origin destination.

Simulation of Passenger Demand

• Stage 1: Simulated passenger demand with 10% rise in mean and standard deviation of passenger demand of Stage 0

• Stage 2: Simulated passenger demand with 10% rise in mean and standard deviation of passenger demand of Stage 1

• Stage 3: Simulated passenger demand with 10% rise in mean and standard deviation of passenger demand of Stage 2

• Stage 4: Simulated passenger demand with 10% rise in mean and standard deviation of passenger demand of Stage 3

• Stage 5: Simulated passenger demand with 10% rise in mean and standard deviation of passenger demand of Stage 4

Simulation of Passenger Demand

Revenue Management System

Current Data Historical Data

Forecaster

Optimizer

System Recommendations

Performance Monitoring and Reports

• We choose train no.2901 running between a metro and a mini metro

• We use April 2005-07 booking data as inputs to predict the passenger arrivals of April 2008

• As maximum passengers travel from origin to destination we concentrate on that sector and do our analysis

Forecasting Module

• We collect data from CRIS (Center for Railways Information System) on passenger arrivals for each fare class and for all origin destination pairs

• The key elements of the data format includes journey date, booking date, class, passenger source, passenger destination and booked passengers

Forecasting Module

• From this format we generate two major variables for each fare class

(1) Days before departure

(2) Cumulative booking of passengers

• We build booking curves for each fare class and for origin and destination for April 2005-08

Forecasting Module

• Analyzing the booking curves we divide the booking horizon into six parts

• D-21(21 days prior to departure), D-14(14 days prior to departure), D-7 (7days prior to departure), D-2(2 days prior to departure), D-1(1 day prior to departure) and D0(day of departure)

• We use additive and incremental pick up methods to forecast final day bookings of April 2008 for each fare class

• We measure the forecast accuracy by Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE)

Forecasting Method

Error Measurement

• Mean Absolute Deviation (MAD)• Find absolute difference between forecast and actual

• Average over all observations

• Mean Absolute Percentage Error (MAPE)

• Find absolute difference between forecast and actual

• Find percentage of actual

• Average over all observations

• These forecasting methods work efficiently in case of 2nd AC and 3rd AC followed by sleeper but not accurate for 1st AC

• Incremental performs better than additive method

• It produces MAPE less than 10% for 1,2 or 7 days prior to departure

Forecasting Module

• Mean point forecast is difficult to predict

• We calculate the forecast ranges of passenger arrivals based on the standard deviation of historical passenger bookings

and check the percentage of forecast accuracy

Forecasting Module

Recommendations

• Forecasting module performs well for 1 or 2 days prior to departure

• So intermediate stations quota can be released 1 or 2 days before departure date

• Excess demand of a train can be shifted to another train sharing the same origin and destination

• If no show information is stored in the data warehouse we will get better patterns regarding passenger behaviour and can analyze booking process more efficiently

Thank You