The purpose of this paper is to attempt to put forth an innovative geographic information system (GIS)‐based methodology for demarcating stretches of mountain roads with…
The purpose of this paper is to attempt to put forth an innovative geographic information system (GIS)‐based methodology for demarcating stretches of mountain roads with differential probability of road accidents. The proposed methodology has been tested in a sample road network of Uttarkashi district in Uttaranchal (India) and exhibits potential of reducing the frequency of road accidents by adopting suitable site‐specific measures along accident‐prone stretches.
The paper is based on the hypothesis that road accidents in the mountain roads are largely due to the three basic road parameters that distinguish mountain roads from those in the plains; sinuosity, gradient, and width. The sinuosity of the road is calculated for every 500 meter stretch of the road map layer while for delineating the gradient of the road topographic data of Survey of India maps have been used. The paper utilises GIS‐based environment for correlating these parameters and delineating accident‐prone road stretches.
The proposed new methodology for delineating differential accident risk in mountain roads has been utilised for demarcating road stretches with differential probability of road accidents and the output has been correlated with the actual road accident database of Uttarkashi district in Uttaranchal. The correlation exhibits the potential of this methodology for practical mitigative planning‐related purposes. The same can also be utilised for better aligning the planned roads.
Human factor is the most important determinant of road accidents and non‐incorporation of this parameter is the biggest limitation of the proposed methodology. Further, the effectiveness of the proposed methodology is the function of the validity of the hypothesis. The methodology is, however, highly flexible and has ample scope for accommodating other parameters as well. The effectiveness of the output is, however, a function of the accuracy of the input maps. Road layer considered in this paper has been prepared from the maps available with the State Government Department (Public Works Department) and their alignment does not depict the ground details. Input road layer prepared with precision Geographical Positioning System (GPS), preferably differential, mapping would produce more realistic results. The positions of the past accident sites for the purpose of correlations are taken from the data provided by the State Police Department and these are not very accurately determined. GPS‐based database of the accident locations would help in effective correlations.
The methodology proposed in this paper is an attempt to scientifically delineate differential accident‐prone stretches of the mountain roads. This would pave the way for implementation of site‐specific measures for reducing probability of road accidents and better aligning of the proposed roads.
Previously a large number of workers have used GIS‐based techniques for delineating hazard and risk related largely to landslides, floods and earthquakes but the same has never been employed for delineating road accident risk. The methodology is simple, unique, original and functional and has immense practical utility for reducing the menace of road accidents in mountain roads.
Analysis of data on road accidents collected from different sources brings forth important characteristics related to the nature of accidents. Based on this, the fatality…
Analysis of data on road accidents collected from different sources brings forth important characteristics related to the nature of accidents. Based on this, the fatality index (FI) is defined as the ratio of fatalities to injuries in accidents. An increase in FI is indicative of fatalities in accidents. High FI is observed to correlate positively with difficult terrain, slow response and poor medical facilities. FI therefore represents an important indicator for planning initiatives to reduce fatalities related to road accidents.