Introduction
It has been estimated that over 300,000 persons die and 10-15 million persons are injured every single year in road accidents throughout the world. Detailed analyses of global accident statistics indicate that fatality rates per licensed vehicle in developing countries are very high in comparison with the industrialized countries. Moreover, road accidents have been shown to cost around 1% of annual gross national product (GNP) resources of the developing countries, which they can ill-afford to lose. Hence it is necessary to incorporate steps, which can reduce road accident rates and implement mitigating actions, which can be taken to reduce the number and severity of road accidents. (Baguley et al, 1994)

Various road safety strategies and countermeasures have been used at different stages of network development. This method of seeking to prevent road accidents mainly involves conscious planning, design and operations of roads. One of the most important factors in this method is the systematic identification and treatment of hazardous locations. The main objective of the study presented in this paper is to develop a model necessary to identify these hazardous locations on roads commonly termed as black spots. In general, the various factors that cause accidents can be broadly categorized into road related, vehicle related and driver related. In this paper, an attempt is made to implement the road related factors for predicting the accident prone points (black spots) on roads and thus help in identifying the required remedial measures (Kalga and Silanda 2002).

Methodologies for predicting accidents have been widely studied in the past. The prediction models are mostly causative types in which the number of accidents is taken as a function of number of independent variables. Recently there have been studies to identify accident-prone locations using fuzzy and neural network classifier approaches. The most common methodological approach used in the research efforts to model interaction between the highway geometries, traffic characterization and accident frequency is regression analysis. The ease of modeling readily favors the regression approaches. However such methods are highly dependent on traffic flow data like Average Daily Traffic (ADT) and the data collected by the traffic police from the accident sites. But traffic flow data are rarely available in sufficient quantity or accuracy to justify these regression approaches. Moreover the traffic police may not be able to collect all the necessary data required to carry out the analysis using that data (Kalga and Silanda 2002).

Consequently considering all the factors mentioned above, it is necessary to develop a model which can assist in predicting black spots on a given road network without the requirement of the traffic flow data with considerable accuracy. This paper describes a model developed to identify black spots on roads using prioritization and GIS. A road network is distributed over a given area. Hence it always posses a 'spatial characteristic' i.e., it always has the geographic locations associated with it. Thus, in order to model a road network, an information system capable of processing spatial data is required. A GIS can easily handle, store, analyze, manipulate and retrieve spatial data. Therefore a model for identifying accident-prone location on roads can be easily implemented using a GIS.

Methodology
The model described in this paper requires a map of the desired road network digitized in a suitable form and certain specified road attributes to carry out prioritization The analysis then identifies accident black spots on the given road network. While carrying out the analysis the model only incorporates the road related factors such as road geometries, which lead to accidents. The factors considered for evaluating accident prone locations on road are as follows:

• Road width.
• Number of lanes.
• Approximate number of vehicles per day.
• Type of road.
• Drainage facilities.
• Surface condition of the pavement.
• Frequent vehicle type.
• Presence of shoulders, edge obstructions, median barriers and ribbon development.
• Radius of horizontal curve.

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