Predictive Model for Accident Management System

Ramya V , Pratyasha , Priyanka K , Padmavati Badiger

Abstract:Cities are facing challenges with the projected population growth and the resulting increased urban travel demand. Road safety is a major issue in urban planning. Much of the empirical research on road safety and determining the probability of accidents has focused on the accident events. Road accident is one of the crucial areas of research in India. While human error and mechanical failure are common causes of road traffic accidents, the importance of spatial factors has been underestimated. Classifications of road accident are generally based on the available data associated with the accident itself such as time of day, type of injury, type of vehicle, etc. This can often limit the understanding of the complexities of road accidents which can be the outcome of a number of environmental, social and economic factors neglected by the standard accident data collection. Analysis of road accidents, particularly the spatial patterns of accidents requires further attention. On the other hand, one of the most burning issues is about predicting traffic accidents. Predicting accidents on the road can be achieved by classification analysis, a data mining procedure requiring enough data to build a learning model. To improve predicting accuracy, corrected data is classified into several groups, to which classification analysis is applied. A variety of research has been done on data collected through police records covering a limited portion of highways. Evolution of traffic flow on highways, especially under accidents, is crucial to understanding and reducing the impact of accidents. Association rule mining technique is applied to identify circumstances in which an accident may occur and the results can be utilized to put some accident prevention efforts in the areas identified for different categories of accidents to overcome the number of accidents. This system uses a predictive model for accident prediction, analyses accidents caused by certain key factors and also concentrates specifically on accidents that occur on highways. This system can be useful for any common man for information which is provided in a user friendly GUI and also help the traffic officer for decision making and reporting of an accident. The dataset will help to the extraction of rules from pattern observed in accidents related to spatial characteristics. This system also provides guidance to government authorities involved in road planning. Keywords:Prediction, Analysis, Regression formula, Client-server model

Title:	Predictive Model for Accident Management System
Author Name:	Ramya V , Pratyasha , Priyanka K , Padmavati Badiger
Abstract:	Abstract:Cities are facing challenges with the projected population growth and the resulting increased urban travel demand. Road safety is a major issue in urban planning. Much of the empirical research on road safety and determining the probability of accidents has focused on the accident events. Road accident is one of the crucial areas of research in India. While human error and mechanical failure are common causes of road traffic accidents, the importance of spatial factors has been underestimated. Classifications of road accident are generally based on the available data associated with the accident itself such as time of day, type of injury, type of vehicle, etc. This can often limit the understanding of the complexities of road accidents which can be the outcome of a number of environmental, social and economic factors neglected by the standard accident data collection. Analysis of road accidents, particularly the spatial patterns of accidents requires further attention. On the other hand, one of the most burning issues is about predicting traffic accidents. Predicting accidents on the road can be achieved by classification analysis, a data mining procedure requiring enough data to build a learning model. To improve predicting accuracy, corrected data is classified into several groups, to which classification analysis is applied. A variety of research has been done on data collected through police records covering a limited portion of highways. Evolution of traffic flow on highways, especially under accidents, is crucial to understanding and reducing the impact of accidents. Association rule mining technique is applied to identify circumstances in which an accident may occur and the results can be utilized to put some accident prevention efforts in the areas identified for different categories of accidents to overcome the number of accidents. This system uses a predictive model for accident prediction, analyses accidents caused by certain key factors and also concentrates specifically on accidents that occur on highways. This system can be useful for any common man for information which is provided in a user friendly GUI and also help the traffic officer for decision making and reporting of an accident. The dataset will help to the extraction of rules from pattern observed in accidents related to spatial characteristics. This system also provides guidance to government authorities involved in road planning. Keywords:Prediction, Analysis, Regression formula, Client-server model
Cite this article:	Ramya V , Pratyasha , Priyanka K , Padmavati Badiger , " Predictive Model for Accident Management System " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 6, Issue 2, March - April 2017 , pp. 163-169 , ISSN 2278-6856.
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