INDIA RAILWAYS GIS-BASED DECISION-SUPPORT SYSTEM

This paper describes the implementation of Phase I of a Long Range Decision-Support System for the Indian Railways (IR) which has been under development during the last two years. The system incorporates GIS (ArcView) as a user interface, as means of storing and retrieving system inventory and facility management data, as a link to transportation models, and as an interface to a set of evaluation tools for investment and marketing decisions. This set of functions provides an user- oriented system that has greatly improved information available to IR managers over the previous system, which depended on manual, hard copy reports. The system added flexibility and analytic power as well as spatial data that was not previously available in a timely fashion to managers. The system has the potential to identify major costs savings in achieving rail line capacity expansion and contribute to the process of changing IR priorities to achieve more cost-effective investments and marketing strategies. The paper will describe data base and modelling issues as well as GIS and analysis issues.

INDIA RAILWAYS GIS-BASED DECISION-SUPPORT SYSTEM



Background



The new economic environment in India has given rise to a 

growth rate of GDP averaging 5.6 % per year and growth of 7 to 

9 % annually in surface transport demand.  Export growth is 

averaging 18-21%.  This growth is creating pressures on both 

the road and rail modes and it is anticipated that the railway 

will be called on to handle 125-150 million tons of additional 

freight (a 34-41 percent increase or average annual growth of 

more than 6%) and an even faster increase in passenger flows 

by the year 2000.  Therefore, identification and evaluation of the 

most cost-efficient means for achieving capacity expansion is a 

top priority for IR.



With this heavy pressure for growth and a shortage of 

investment funds, effective planning tools are needed by IR to 

provide a comprehensive basis for the screening and 

evaluation of proposed improvement projects and to provide 

better forecasts of traffic for each corridor, taking into account 

the competition from road haulage. Since the capacity 

expansion alternatives for Indian Railways lie in a 

complicated combination of investments, there is a need for 

relatively sophisticated but cost-effective methods to determine 

the relative priorities of system-wide investments. The IR also 

identified a need for access to more market information to help 

it compete for the most profitable business in the future freight 

market.



Brief Description of Indian Railways



The Indian Railway is a large system by international 

standards with 62,000 route kilometers and transportation 

revenues of Rs 758 billion (U. S. $21.6 billion) in 1994-1995.  It 

carried 365 million tons of freight in that year, an increase of 3 

% over the preceding year, which reflects the continuing 

growth of railway traffic over the last few years.  The railway is 

a unique mixture of long haul bulk freight operations and high 

volume inter-city passenger operations.  It is a continent-wide 

railway with average freight hauls of about 705 km compared 

to the European average freight hauls of 200-400 km.  It is both 

a short-haul suburban mass transit railway and a long-haul 

inter-city passenger railway.



Despite the growth in rail services and the successful 

introduction of modern container operations, there has been a 

relative decline of rail output to road output over the last 15 

years.  This reflects the specialization of IR in carrying low-

valued, long-haul bulk cargoes, such as coal and the very low 

unit revenues from inter-city passenger operations. Road 

competition for freight has also been very effective, although it 

has lessened in the last two years due to congested road 

conditions.  The planned construction of better intercity roads 

and expressways will increase the challenge for the Railway to 

become more competitive.



The Long Range Decision-Support System



In 1994, IR management decided to undertake the development 

of a decision-support system which would provide the tools to 

carry out evaluations of all the critical factors and help set 

investment priorities that had been missing in the past.  The 

IR called this a Long Range Decision Support System (LRDSS) 

and enlisted assistance from the World Bank for its design and 

implementation.  It was conducted as an institution-building 

exercise with a substantial training component, involving the 

creation of a well-trained multi-disciplinary LRDSS team 

supervised by a Steering Committee in the Railway Board. 



The primary objective of LRDSS Phase I is to develop a decision 

support system (DSS) for the management of the Indian 

Railways  that would allow it to evaluate decisions that affect 

railway capacity in a comprehensive, system-wide, multi-

modal context and to evaluate potentially profitable markets for 

railway service. The system must be capable of evaluating a 

complicated and inter-related set of investments  (e.g.,  gauge 

changing, improved signaling systems, lengthened sidings, 

urban bypasses, high horsepower locomotives, low tare weight 

high axle load freight wagons, and train operations policies 

designed to maximize the capacity of the track). 



The analysis objectives for the DSS are to: (a) forecast traffic 

flows in total and by major category for freight traffic and 

passenger traffic; (b) eliminate bottlenecks by taking cost-

effective measures to improve utilization of existing track and 

rolling stock assets; (c) evolve a least-cost operating strategy for 

movement of traffic between pairs of points served by more 

than one route; (d) determine priorities among already-

sanctioned transport capacity augmentation projects; (e) select 

new, high priority investments for increasing total system 

throughput within prevailing budget constraints; and (f) 

achieve a revenue maximization strategy within broad policies 

of transportation on Indian Railways.



To meet these objectives the decision-support system requires 

of a set of analytic tools and models integrated into a user-

friendly  system with access to a wide range of data.  The most 

appropriate and cost-effective framework for the DSS was 

found to be a combination of commercial simulation software, 

specially-designed system optimization software, specially-

designed database structure and a Geographic Information 

Systems (GIS) which provided both customized dialog with the 

users and the ability to use a mixture of map and table-based 

information to identify and evaluate the best alternatives. 



The DSS selected for Indian Railways was an advanced version 

of a similar DSS developed for China Railways in the early 

1990s (Cook, 1993).  The main differences from the earlier 

system, were a much closer integration of the GIS with the 

rest of the DSS (e.g. input screens for the investment options 

database, broader simulation capability, customized menus for 

evaluation and closer linkage of model inputs and outputs with 

GIS displays of the transportation network).  The structure of 

the DSS is shown in Figure 1.



Implementation of the DSS



The analytical tools for the DSS are structured around six key 

strategic modules, which are capable of analysis of 

investments over a 20-year planning period with detailed 

analysis for the first five years and for every fifth year 

thereafter.  The six modules are:



	a)	Traffic Forecasting Module: This module forecasts 

goods and passenger traffic demand between major 

origin-destination pairs for various commodities 

under different assumed demand scenarios;



	b)	Facility Performance Module:  This module 

estimates capacity, cost and transit time for existing 

and proposed, converted and new rail lines, yards, 

transhipment points and other congested facilities.  

For rail line costs and delay functions, it uses the 

results of a detailed rail line simulation model 

(RAILS);



	c)	Traffic Assignment Module: This module ssigns the 

forecasted traffic and compute financial costs for the 

major railway network under different assumed 

scenarios of investment and demand.  It includes a 

network-wide model based on non-linear 

programming;



	d)	Cost-Benefit Analysis Module:  This module provides 

an economic and financial cost-benefit analysis in 

summary form for each proposed investment 

alternative over a 20-year period under a selected 

demand scenario;



	e)	Financial Forecasting Module: This module 

translates the results of the above modules into a 

summary of the costs and revenues of IR for each 

major commodity group and for passengers.



	f)	Market Analysis Module:  This module stores the 

results of the shipper survey and analyzes 

information on cost and traffic relevant to shipper 

decisions on the choice of road or rail for goods 

movements.  It includes a basic mode choice model, 

calibrated from the shipper survey results.



These modules are all linked together with a GIS-based user 

interface, which provides graphics, dialog boxes, spatial 

analysis tools and other decision-support features (see Figures 

2 and 3 for examples).



As the reader can see, the LRDSS is a relatively ambitious 

undertaking, especially for an organization of the size of 

Indian Railways, with limited data processing facilities. The 

implementation of the system, therefore, went through some 

time-consuming data collection and model calibration steps.  

There were several significant issues that slowed down the 

implementation. The most important of these was the 

significant unanticipated data entry and data cleanup 

problems (traffic and railway line operations data) and the 

discovery of major data gaps (road traffic and shipper 

information) that needed to be filled by formal surveys.  



Secondary issues were software bugs and the linkage of rail 

network data between the GIS graphics and the model data 

bases.  It took approximately eighteen months to implement 

Phase I, including the first three modules of the DSS (with 

initial calibrations and four months of team training), but 

without the formal survey data and the broader road-rail 

aspects of the DSS which are needed for the remaining 

modules.  Also resources were diverted from the 

implementation of the three remaining modules to deal with 

the data cleanup problems in Phase I.  A second phase has 

been approved by IR to carry out the surveys to fill in the data 

gaps and complete and calibrate the remaining modules. 



Although the full objectives of the DSS were only partly 

achieved in Phase I, the foundation has been laid for a system 

that meets the strategic capacity planning needs of IR.  A 

substantial database has been built up, an initial set of 

hardware and software is now in place, both line capacity and 

strategic models have been preliminarily calibrated and basic 

forecasts and traffic assignment have been carried out for a 

key forecast year (2000).  Also a multi-disciplinary team has 

been created in the Railway Board to utilize the LRDSS for 

investment and policy analysis.



Initial Results of LRDSS



The initial results of the LRDSS Phase I are of four types: (a) 

provision of data access to planners and managers that did not 

exist before; (b) creation of  traffic forecasts by origin-

destination that did did not exist before; (c) analysis of system 

bottlenecks, both with existing train routing procedures and 

with alternative routings and (d) identification of alternative 

locations for system improvements that could be more cost-

effective than the previously identified alternatives.  The 

anticipated outputs of Phase II will identify and evaluate even 

more cost-effective options for IR management, and give 

access by managers to an even wider range of data that they 

could not access before the LRDSS.



The data on the IR rail system available to planners and 

managers in the Railway Board has been very limited before 

the LRDSS.  It has been primarily in the form of reports and 

with many hidden assumptions and biases.  The statistics 

available were of an aggregated nature that did not allow 

much analysis and more detailed data took a long time to 

acquire.  With the LRDSS much of the basic system data (track, 

traffic, facilities, etc.) is now available in both map and table 

form to anyone in the Board or on the support staff with access 

to a computer with LRDSS software and databases.  (It will be 

even more available through local area networks in the 

future.)  This is a major increase in information flow in the 

Railway.



Traffic data is now available by origin and destination to 

managers.  This data was previously stored on tapes but in an 

unreadable format and not consistent between the zonal 

railways, and therefore not available to anyone who wanted to 

use it.  The availablity of this data and the capability to display 

it in both map and table form is a significant tool for planners 

and managers.



The ability to identify system bottlenecks is the first step in 

identifying cost-effective solutions to system problems.  This 

ability of the LRDSS is also tied to scenario analysis or "what 

if" types of analyses, that combine the power of a systems 

model (based on detailed simulation of operations on selected 

links) with the power of a GIS to display the results in multiple 

views and tables.



Finally, the identification of potentially cost-effective 

alternatives, even without the cost-benefit framework to be 

provided in Phase II, is a major step forward for IR managers.  

This gives them the scope to investigate a specific range of 

options that is broader than the high-cost construction 

alternatives, that have been the focus of previous alternatives 

for system capacity improvements and that relates specifically 

to the forecast traffic flows that they will have to carry on 

different routes through the rail system.  The savings from 

identifying better investment alternatives can run into the 

billions of dollars in investments as previously found by China 

Railways.



With the LRDSS, the preliminary evidence is that the Railway 

Board of the IR will have an improved ability to make better 

decisions and get more productivity out of their railway 

system.  Phase II will expand that capability even more.



REFERENCES



Cook, Peter, "The Use of GIS in Improving the Cost-

Effectiveness in Transport Investment Decisions: The China 

Railway Example," Pacific Rim TransTech Conference 

Proceedings, Volume II, July 25-28, 1993.