- Data matching
The accident data in local grid coordinates are converted to latitudes and longitudes, and exported as X, Y coordinates and overlaid on the expressway layer. One problem in accident analysis is the quality of data. The records are based on the police-reported accidents and there may be wrongly reported. However these can be easy seen on the GIS maps. Some modifications may be applied to correct these wrong entries. An example of an accident distribution is as shown in Figure 1.
Methodology
In this study, the analysis has not been confined to any particular class or type of accidents and rather towards utilizing the open nature of GIS in data manipulation. Hence it is decided to consider all the crashes initially and determine accident-prone locations by developing density maps.
- Density Maps
Density maps are produced for all the years that are considered from 1992 to 2000 with the input data as individual accident points. Density maps show where the highest concentration of a particular type of accidents. They are useful for looking at patterns rather than at locations of individual features. It creates a continuous raster surface from a set of input point features representing a magnitude per unit area, as in our case the number of accidents per square kilometer. Hence by density maps the places where accidents are clustered can be easily distinguished.
- Identification of accident-prone areas
From the density maps produced for different years, the accident-prone areas can be easily distinguished. In order to get the real locations wherein the accidents are highly clustered all through these years, the raster layers are added up together. Thus the summed up layer gives us a density map showing accident density distribution.
For better localization of the potential areas the resulting layer is reclassified i.e. producing a different raster layer by modifying classification range, color coding etc. Reclassification makes it easy to understand the distributions and also to make decisions from the displaying map. Figure 2, shows areas in Singapore expressways where accidents are highly clustered.
As these areas have been determined based on 5 years of data, it can be argued that these areas may have certain factors which contribute to accidents recurrence there. The accidents may be clustered over an area but these can be caused by factors in a specific location. Hence the next step after identification of high accident locations is to perform a detailed safety analysis on these areas. Usually much less thought has been given to the safety analysis stage that follows. However it will help to determine abnormal accident pattern and would lead to selection of sites for appropriate remedial measures for the improvement of safety. This analysis of collision trends of specific accident types can be flexibly done and visually displayed using GIS.
Fig.2: Identified Accident-Prone Areas: Singapore Expressways
Diagnosis
Based on the available attributes of the accident data it has been observed that the distribution of accidents varies depending on the time of a day, the type of road surface at the time of accident, the type and number of vehicle involved in the collision and so on. The following section describes how GIS software may be used to highlight specific accident types.
- Accident based on type of road surface
Pavement surface characteristic is one of the important factors, which determines the safety of a vehicle when negotiating a curve or at the time of sudden application of brakes. From the present study it has been found that major percentage of accidents occurred at both accident-prone and rest of areas when the road surface condition was dry. But comparing the percentage of accidents which occurred when the surface is wet it has been found that the accident-prone areas contribute more, leaving us the clue for viewing such accident location distributions to look for the factors and take countermeasures.
Fig.3: Accident-Prone Areas: Spatial distribution of accidents by road surface