Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks

2006 ◽  
Vol 38 (3) ◽  
pp. 434-444 ◽  
Author(s):  
Dursun Delen ◽  
Ramesh Sharda ◽  
Max Bessonov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Artificial Neural

Artificial Neural Networks and Logit Models for Traffic Safety Analysis of Toll Plazas

2002 ◽  
Vol 1784 (1) ◽  
pp. 115-125 ◽  
Author(s):  
Hassan T. Abdelwahab ◽  
Mohamed A. Abdel-Aty
Keyword(s):  
Neural Networks ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Logit Models ◽  
Severe Injuries ◽  
Toll Plazas ◽  
Artificial Neural ◽  
Toll Plaza ◽  
The Impact

Little research has been conducted to evaluate the traffic safety of toll plazas and the impact of electronic toll collection (ETC) systems on highway safety, but analyses indicate that toll plazas do contribute to traffic accidents. Traffic safety issues related to toll plazas and ETC systems were studied using the 1999 and 2000 toll plaza traffic accident reports of the Central Florida expressway system. The analysis focused on accident location with respect to the plaza structure (before, at, after plaza) and driver injury severity (no injury, possible, evident, severe injuries). Two well-known artificial neural network (ANN) paradigms were investigated: the Multi-Layer Perceptron and Radial Basis Functions neural networks. The performance of ANN was compared with calibrated logit models. Modeling results showed that vehicles equipped with ETC devices, especially medium/heavy-duty trucks, have higher risk of being involved in accidents at the toll plaza structure. Also, main-line toll plazas have a higher percentage of accident occurrence upstream of the toll plaza. In terms of driver injury severity, ETC users have a higher chance of being injured when involved in an accident. Older drivers tend to have higher risk of experiencing more severe injuries than younger drivers. Female drivers have a higher chance of experiencing a severe injury than do male drivers.

scholarly journals Predicting Injury Severity of Angle Crashes Involving Two Vehicles at Unsignalized Intersections Using Artificial Neural Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 3871-3880
Author(s):  
S. A. Arhin ◽  
A. Gatiba
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Injury Severity ◽  
Economic Cost ◽  
The United States ◽  
Sigmoid Function ◽  
Unsignalized Intersections ◽  
Fatal Crashes ◽  
Unit Function ◽  
Artificial Neural

In 2015, about 20% of the 52,231 fatal crashes that occurred in the United States occurred at unsignalized intersections. The economic cost of these fatalities have been estimated to be in the millions of dollars. In order to mitigate the occurrence of theses crashes, it is necessary to investigate their predictability based on the pertinent factors and circumstances that might have contributed to their occurrence. This study focuses on the development of models to predict injury severity of angle crashes at unsignalized intersections using artificial neural networks (ANNs). The models were developed based on 3,307 crashes that occurred from 2008 to 2015. Twenty-five different ANN models were developed. The most accurate model predicted the severity of an injury sustained in a crash with an accuracy of 85.62%. This model has 3 hidden layers with 5, 10, and 5 neurons, respectively. The activation functions in the hidden and output layers are the rectilinear unit function and sigmoid function, respectively.

scholarly journals A Comparative Analysis and Prediction of Traffic Accident Causalities in the Sultanate of Oman using Artificial Neural Networks and Statistical methods

1998 ◽  
Vol 3 ◽  
pp. 11 ◽  
Author(s):  
Galal A. Ali ◽  
Saleh M. Al-Alawi ◽  
Charles S.Bakheit
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Regression Equations ◽  
Sultanate Of Oman ◽  
Related Factors ◽  
Population Number ◽  
Artificial Neural ◽  
On The Road

Traffic accidents are among the major causes of death in the Sultanate of Oman This is particularly the case in the age group of I6 to 25. Studies indicate that, in spite of Oman's high population-per-vehicle ratio, its fatality rate per l0,000 vehicles is one of the highest in the world. This alarming Situation underlines the importance of analyzing traffic accident data and predicting accident casualties. Such steps will lead to understanding the underlying causes of traffic accidents, and thereby to devise appropriate measures to reduce the number of car accidents and enhance safety standards. In this paper, a comparative study of car accident casualties in Oman was undertaken. Artificial Neural Networks (ANNs) were used to analyze the data and make predictions of the number of accident casualties. The results were compared with those obtained from the analysis and predictions by regression techniques. Both approaches attempted to model accident casualties using historical  data on related factors, such as population, number of cars on the road and so on, covering the period from I976 to 1994. Forecasts for the years 1995 to 2000 were made using ANNs and regression equations. The results from ANNs provided the best fit for the data. However, it was found that ANNs gave lower forecasts relative to those obtained by the regression methods used, indicating that ANNs are suitable for interpolation but their use for extrapolation may be limited. Nevertheless, the study showed that ANNs provide a potentially powerful tool in analyzing and forecasting traffic accidents and casualties.

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