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Abstract

Background: Traffic accidents have become a social scourge more serious than may be expected. Traffic accidents are greatly concerning for all members of society, and have become one of its most important problems, draining human and material resources. This is in addition to the incurred problems of social, psychological and material losses. Road traffic accidents are increasingly being recognized as a growing public health problem in Qatar. Objective: The main aim of this work is to identify the factors affecting the road accidents in Qatar, and the model that predicts the number of road accidents by year 2022. Method: The number of road accidents can be predicting by the Accident Prediction Model - a mathematical formula describing the relation between the safety level of existing roads (i.e. crashes, victims, injured, fatalities), and variables that explain this level (population ,driving license, number of vehicles). The methods that describe the existing case in Qatar were the multiple linear regression model and the artificial neutral network. Those methods were analyzed and their findings compared. Results: In this study we used multiple linear regression (using number of vehicles, type of vehicles, population, number of driving license) and neural networks to both back-cast (1992-2012) and forecast accidents (2012-2022) in the state of Qatar. The results estimated that if there is no intervention the number of accidents is expected to reach 320,000 by 2022. Conclusions: The main causes of road accidents have remained much the same over time with reckless driving, speeding and lane changing errors (at roundabouts and on higher speed roads) responsible for more than 90% of accidents. Pedestrian fatalities and accidents are of particular concern in Qatar and the number of heavy vehicles using roads has also increased, causing additional safety issues. Road safety has been given high priority for some years in Qatar through extensive safety and awareness campaigns and more aggressive law enforcement, which over the last three years has helped reduce fatalities to their lowest mark in two decades.

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/content/papers/10.5339/qfarf.2012.AHP31
2012-10-01
2020-07-05
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2012.AHP31
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