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This paper highlights the use of Geographical Information System for Investment Planning in the Rail transportation sector. 

Indian Railways is one of the largest transport systems in the world with a total of over 60,000 track kilometers spanning the length and breadth of India. About 14 million passengers - about the population of Australia- and over one million tons of freight traffic move over this network every day with a fleet of 6100 locomotives and half a million fleet cars. Indian Railways, is truly the lifeline of India and contributes over 1% of the national GDP, with its annual revenues aggregating to nearly 10 billion dollars.

One of the peculiarities of basic infrastructure - transport, power transmission and telecommunications, - is that it forms and operates as a part of a geographically spread out network industry. As a result, a change in infrastructure / operating practices in one part of the network has a significant impact over other parts of the network. This "ripple effect" translates into complexities for investment planning wherein benefits visualized through additional investment often do not result in achieving the desired objectives. In the absence of a strategic model that 'sees' the network as a whole, infrastructure-related investments tend to result in optimizing sub-systems and simultaneously rendering the entire network system sub-optimal. A realization of this 'ripple effect' and problems of subsystem optimization has lead to the application of operation research based transportation models.

Growth of transport infrastructure involves a lifecycle of planning, design, construction followed by operation and maintenance (see Adams et al 1992) and spatial data is central to each of these phases. An overwhelming amount of transportation data is location based and geographically referenced and in the light of this, GIS has emerged as the optimal technology to manage this data. There is also a growing realization among transportation experts that design and development of an integrated transport information system requires Geographical Information System. 

Research worldwide has attempted to enhance the power of GIS by adding /attaching 'TM' i.e. transportation modeling to the GIS so as to develop an integrated Geographical Information System for transportation. However, looking at the complexities in network modeling in general and peculiarities in transportation modeling in particular, it is not feasible to develop a universal GIS-T for meeting the requirements of all the users. Dedicated GIS-T software for meeting specific user requirements is however, a strong possibility. One such successful case study is the Indian Railways Long-Range Decision Support System (LRDSS)

The long-range Decision Support System (LRDSS) project was started in 1994 as a joint effort between World Bank and a team of Indian Railway Officers. The objective of the LRDSS was the development of a scientific and data based system for drawing up investment plans for track, rolling stock, and locomotives for the Indian Railways network. 

The LRDSS has been developed as a decision support tool for facilitating pre-feasibility level analysis of investment alternatives and for prioritization of projects based on their financial viability. In its present form, LRDSS is being used as a strategic planning tool for drawing up long term investment plans for Indian Railways. It provides an objective, information based, interdisciplinary platform for evaluating different investment alternatives aimed at improving the network wide capability of Indian Railways.

A unique feature of the LRDSS is the Avenue based User Interface, which insulates the planner from complex transportation models and gigabytes of network data as well as transport forecasts, and at the same time, provides him with a very powerful tool for long term planning. The GIS capability at the front end provides top-level decision-makers with a usable state of the art investment analysis tool. The GIS front end facilitates quick evaluation of specific technology, policy, and market related initiatives on a system-wide basis. In addition, the GIS is used for doing some preliminary infrastructure related analysis. 

LRDSS consists of six modules and each of these modules is envisaged to work in a stand-alone mode as well as an integrated whole. All the six modules reside at the back end while the front end and the user interface uses ArcView. A brief description of each module is given below:

Traffic Forecasting Module (TFM): This module is used to obtain the total transport demand between pairs of points for different commodities and for different key years. The inputs used by this module include production and consumption forecasts for ten major commodities for the entire country and for the next fifteen years. The data is based on extensive studies done by the Planning Commission and relevant ministries from time to time. In addition, and as a part of the data collection effort, Rail India Technical and Economic Service (RITES) were retained for conducting road survey to assess the road traffic levels that can be attracted to Rail. 

The TFM uses Linear Programming and "Furness" and factoring techniques for different commodities and generates a commodity wise Origin Destination (O-D) traffic forecast matrix. 

The module as well as databases reside at the back end of the User Interface and the front end Arc View is used to generate and view useful maps that bring out the pattern of traffic - both by Rail and Road- between pairs of points. These maps can be generated for different commodities, different pairs of points and different key years. The user interface also enables the user to generate "Origin Analysis" maps and "Destination Analysis" maps for different commodities/years. The sample map below brings out the outflow of traffic from Satna Area in Madhya Pradesh. While this sample map has been drawn for all the commodities, the user interface allows the user to select a specific commodity, a specific key year and a specific originating area.

Maps of the type shown above help the decision-maker to better appreciate the traffic movement potential as well as traffic leads for specific commodities. These outputs help to initiate corrective action and optimally deploy resources - wagons, locomotives so as to improve the market share. The quantum of traffic is gauged by the thickness of the red lines as shown above. 

In addition to its presentation power, the User Interface can be used for altering the assumptions used during traffic forecasting and for running complex LP based models at the back end for generation of the O-D traffic forecast by commodity.

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