A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Govardhan, Dr. A.
- A Comparision Study on Traffic Risk Assessment and Road Safety Analysis Using Spatial Data Mining Techniques
Authors
1 Department of Computer Science and Engineering, JNTU, Hyderabad, IN
2 Department of Computer Science and Engineering, JNTU College of Engineering, Hyderabad, IN
Source
Data Mining and Knowledge Engineering, Vol 9, No 6 (2017), Pagination: 126-130Abstract
Road safety and Road accidents are the essential area of study and research in present days. Different study research has been going on but accident graph is not reducing. The foremost among purpose of this research is to this research is to improve the road safety, identify traffic crash analysis and forecasting the accident prone zone with the help of spatial data mining algorithms and techniques. Road accidents causing damage and massive losses for our community and plenty of government assets drain away every year. Spatial data mining became a convenient undertake, highly demanding, and the flourishing research field. A massive amount of spatial and temporal large data have gather in different areas like, Medical Imaging, Traffic Control, Environmental Studies, Remote sensing, Crime Hotspots, Warming of Ocean, Agriculture land grading, Analysis forest extent changes, Navigation, Remote sensing and Image database exploration. This paper emphasis on the distinguishing of SDM and spatial objects, the feature of road accidents and their relation with the geographical environment. This research analysis allows identifying the road difficulties in order to put forward safety measures. Identify the regions with a frequently happening and estimate acuteness of risk then analyze and explain this risk based on geographical location. With the help of spatial autocorrelation, spatial clustering, spatial classification, to discover hidden patterns and predict, controlling and reduce future accidents.
Keywords
Spatial Data Mining, Road Safety, Decision Tree Algorithm, Clustering, Association Rule Mining, SDM.
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