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Goutam Dutta 1 Priyanko Ghosh 1 1 Indian Institute of Management Ahmedabad-380015 India

A Passenger Revenue Management System (RMS) for a National Railway in an Emerging Asian Economy (NREAE). Goutam Dutta 1 Priyanko Ghosh 1 1 Indian Institute of Management Ahmedabad-380015 India. Outline. Introduction and Motivation Literature Search Current Reservation System in NREAE

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Goutam Dutta 1 Priyanko Ghosh 1 1 Indian Institute of Management Ahmedabad-380015 India

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  1. A Passenger Revenue Management System (RMS)for a National Railway in an Emerging Asian Economy (NREAE) Goutam Dutta1 Priyanko Ghosh1 1Indian Institute of Management Ahmedabad-380015 India

  2. 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

  3. 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

  4. 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

  5. Introduction to NREAE • One of the five largest in the world • Runs 14000 trains daily with 9000 passenger trains • 30 million passengers travel daily in 7083 stations • Revenue about 19 billion USD

  6. 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

  7. 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

  8. Revenue Management System Current Data Historical Data Forecaster Optimizer Performance Monitoring and Reports System Recommendations

  9. Revenue Management in Railways • As per Talluri and Van Ryzin, (2004) following railways are using RM • AMTRACK • SNCF • Eurostar • VIA Rail Canada

  10. Literature Search

  11. 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

  12. 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

  13. Comparison of Implementations

  14. Revenue Management System Current Data Historical Data Forecaster Optimizer Performance Monitoring and Reports System Recommendations

  15. Various Fare Classes of NREAE

  16. 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

  17. 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

  18. Railways booking centers offer seats to Passengers in FCFS basis Booking Process Availability of seat Non availability of seat Confirmed tickets are issued on a regular basis Passenger asks for Tatkal (Urgent)quota (2 days prior of the journey date) Availability of seat Non availability of seat Tickets are not confirmed but overbooked in Reservation Against Cancellations (RAC) and Waiting List (WL) format Confirmed tickets are issued on Tatkal 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.

  19. Revenue Management System Current Data Historical Data Forecaster Optimizer Performance Monitoring and Reports System Recommendations

  20. 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)

  21. PARAMETERS = Revenue for fare class k  Kfor leg (i,j)  Landfor time period t  T = Expected Cancellations for fare class k  Kfor leg (i,j)  Landfor time period t  T = Expected Demand forecasted for fare class k  Kfor 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  Kand for time period t  T = Tatkal booking allowed for fare class k  Kandfor 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  Kand 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

  22. Objective Function + • 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

  23. 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

  24. 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

  25. 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

  26. Optimization Model • 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)

  27. 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

  28. Revenue Management System Current Data Historical Data Forecaster Optimizer Performance Monitoring and Reports System Recommendations

  29. 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

  30. Simulation of Passenger Demand • 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.

  31. 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

  32. Revenue Management System Current Data Historical Data Forecaster Optimizer Performance Monitoring and Reports System Recommendations

  33. Forecasting Module • 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

  34. 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

  35. 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

  36. Forecasting Method • 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)

  37. 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

  38. Forecasting Module • 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

  39. 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

  40. 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

  41. Thank You

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