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Introduction
During the second half of the last century, urban population in India had grown enormously. This has resulted in a steady increase in number of cities with a population of one million and above from 5 in 1951 to 35 in 2001. This level of urbanization has brought in its wake its own problems, especially with regard to its impact on the infrastructure facilities. The urban transportation systems have come under heavy strain affecting the quality of life of urban dwellers. Public transport facilities provided by buses are grossly inadequate to meet the increased travel demand and to provide a good level of service. In such situation, planning of higher order (rail based) mass transit system is essential. The first step in this process is to identify the corridor for the new system. Conventionally, the new rail transit corridors are identified based on land availability, and/or planner's judgement, and then its ridership estimates are made using various techniques, for instance stated preference (SP) technique. However, in many cases these estimates prove to be much higher than the actual patronage observed resulting in huge revenue losses (Bodell 2002). Moreover, such differences in forecast and actual patronage can also be attributed to many other reasons, like lack of integration with other public transport services, ease of station access/ egress (and connectivity with major land uses generating or attracting trips) etc. Hence, a more rational approach to identify demand-oriented rail transit corridor is needed.

In past, many researchers have developed different approaches to identify rail transit corridors. In the study carried out by Clark and Oxley (1991) the rail transit corridors were identified by assigning the origin-destination (O-D) matrix to an assumed spider network. The drawback with the spider network is that it only gives travel desires between different zones and does not give the actual system alignment. A similar approach was adopted by Moorthy (1997), but in addition to spider network the flows were assigned on to a combined network of highway and track guided system and the corridors were obtained based on the assigned flows. Gipps et al. (2001) identified corridors for a new road or railway using convergence of geospatial imaging, softcopy photogrammetry, regional significance analysis and alignment optimization. They obtained the corridors based on land availability and cost considerations, which could be suitable for identifying road corridors. However, consideration of actual travel demand pattern of the city is also required for rail. Besides the limitations listed above, all these approaches did not address the need for an integrated planing i.e. operational, institutional, and physical integration, to achieve the forecasted demand for the new rail system. This is particularly important for a choice rider, who will shift to a new public transport only if it offers a comparable level-of-service. Although this point has been addressed to some extent in the work carried out by Chien and Schonfeld (1998), they used a pre-determined hypothetical network of one rail corridor and perpendicular feeder routes at equal spacing, which may not offer a realistic view.

Considering the limitations of earlier approaches, this paper presents a model for identifying new rail transit corridors for future public transport demand patterns based on user equilibrium approach. The objective of the study is to identify the new rail corridor using GIS, which is optimum from both users' and operator's point of view. The rail corridor is to be aligned to allow the user to travel through shortest path with comparable level-of-service and on high demand corridors to give maximum revenues to the operator. In addition, the type of technology to be used on the newly identified corridor is also to be investigated. Although, identifying the corridor depends not only on demand levels but also on economic and political considerations, these are not within the scope of this paper. GIS has emerged out as a good tool for dealing large and complex data including both spatial and non-spatial, it has promising potential in applying to problems like integrated urban transport planning, rail corridor identification etc. (Verma and Dhingra 2002).

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