عنوان مقاله [English]
Road accidents around the world annually take many human lives or even amputation. Therefore, in order to reduce the indirect economic and social effects of such incidents, urgent action is required in various social, technical and engineering fields. General education, automobile standardization, and technical and geometric correction of roads are among the main measures. In the meantime, the identification of the causes of an accident is more preferable, because the method and the volume of the actions can vary due to a variety of causes. Therefore, in order to reduce the number and severity of road accidents, identifying accident points is one of the first steps. The most probable accident points are points where the number of accidents, including death, injury and damage were high, which should be reduced by taking immunization measures (Road Maintenance & Transportation Organization, 2007:1)
Investigation of accidental points in Iran due to the lack of a systematic planning in identifying and prioritizing these points and lack of proper database which includes history of accidents from all over the country is in poor and inadequate status. Therefore, no valid scientific method is used to identify and prioritize them, and the effectiveness and reduction of disasters in these areas are not evaluated even after allocating money and securing them. This research is an attempt to identify and prioritize the accidental points along the roads of Alborz province, to increase the effectiveness of executive measures and reduce the vulnerability of citizens and passengers of the province.
Alborz province has about 407 km of inter-urban road with the least length of the road in the country, while at the same time, due to the location of the province in the west, northwest and north of the country, it has the first place in the interurban traffic of the country. According to the data released by the Iran road maintenance and transportation Organization between the 16.03.2015 and 04.04.2015, more than 27 million traffic has been registered at the provincial level.The highest number of deceased was due to out-of-town accident with 58 people killed per 100 km in the province of Alborz. Despite having intelligent transportation system and the relatively high number of transportation facilities in the country, this road allocates the first place of road accidents to itself. The study of the above indicators shows that in recent years, along with increasing traffic volume in Alborz province, the number of accidents and consequently injured and killed road accidents has also been increased.Therefore, in order to minimize these accidents, it is necessary to identify the high-risk areas and geographical features of the location of these accidents and identify the quantitative and qualitative aspects of the roads of Alborz province. In this regard, the study of accident rate as well as the analysis of spatial patterns of accident distribution in Alborz is another necessity of this study.
In this research, a reaction-based approach based on accidents recorded in the Disaster Information Management System of Red Crescent during the second half of 1391 (2013) and the first half of 1394 (2015), in addition to an estimation method for dispersion of accident points by GIS spatial analysis have been used to identify and categorize in the under study period. To determine the road accidental points in Alborz province, the kernel density estimation model was used. The kernel density estimation model, includes spatial analyzes in the Arc GIS software, to estimate the density for linear and point effects. This analysis is one of the best ways to identify hot spots, which can be used to identify accident points.
Discussions and Findings
The indicators of the number and severity of the accidents, were determined by the use of presented models. In this research, a total of 617 accidents recorded in the studied time period using spatial information system. Of the total number of reported incidents, there have been 2724 accidents, 48 of which have caused death. The distribution pattern of recorded events indicates the spatial distribution of accidents in the three main roads of the province, including the Karaj-Chalous road (294 accidents), Karaj-Qazvin highways (282 accidents) and Mahdasht-Eshtehard (25 accidents).
Using the kernel density estimation method in the Arc GIS environment, the density of accident points in the province has been studied and the results indicated that in the Karaj-Chalous road and the Karaj-Qazvin highways, the areas are the most accidental ones and the probability of accident is clearly high. The length of the route is 2.5 km in the high accident area of the province and 11.85 km belongs to the accident prone area.
Based on the obtained results from the kernel method, areas were divided into four categories of low accident-prone, relatively accident-prone, accident-prone and high accident-prone then were categorized separately into two main roads of the province based on same four categories:
Karaj – Chalous Road
1. Highly incidental area: This area is in line with Amir Kabir dam and tunnel number four of this road, which was identified with a length of 1.3 km, as one of the high accident-prone roads.
2. Incident area: Includes three distinct districts with a total length of 3.3 km, which is identifiable at the beginning of the dam of Amir Kabir, the length of the dam lake and, finally, a three-way road at 50 km on the Chalous road.
1. Highly incidental area: high accident-prone area of this road is located at the exit of Fakhr Iran Township in 33 km of the highway, which is approximately one kilometer.
2. Incident area: 8.5 km from the Karaj highway to Qazvin, based on the obtained results, consists of six separate zones that can be categorized as the accidental areas of the province. These areas are Bridge of Nazarabad, the area beyond Hashtgerd up to the Bridge of Nazarabad, Hashtgerd Exit, Mamot Industrial Area and Kordan Bridge.
traffic accidents ، accident full range ، Alborz Province ، Suburban roads ، Kernel density estimation method
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