PSO-ANN in preventing traffic collisions: a comparative study

Traffic accident is a global threat which causes health and economic casualties all around the world. Due to the expansion of transportation systems, congestion can lead to spike road accident. Every year thousands of people have died due to traffic accidents. Various technologies have been adopted by modern cities to minimize traffic accidents. Therefore, to ensure people’s safety, the concept of the smart city has been introduced. In a smart city, factors like road, light, and weather conditions are important to consider to predict traffic mishap. Several machine learning models have been implemented in the existing literature to determine and predict traffic collision. But the accuracy is not enough and there exist a lot of challenges in determining the accident. In this paper, an approach of particle swarm optimization with artificial neural network (PSO-ANN) has been proposed to determine traffic collision using the dataset of the transport department of United Kingdom. The performance of PSO-ANN outperforms the existing machine learning model. PSO-ANN model can be adopted in the transportation system to counter traffic accident issues. Random Forest, Naıve Bayes, Nearest Centroid, K-Nearest Neighbor classification have been used to compare with the proposed PSO-ANN model.

Fatal road traffic collisions-An autopsy based study

The Deaths due to Road Traffic Collision has become a Major Public Health issue, hence Understanding the Deaths and the Factors involved is important to prevent Fatalities and at the same time Prevent Road Traffic Collision in General. The present Study is a Retrospective Study conducted during the period 2013 to November 2020. Road Traffic Accidents constituted 39.35%[n-1168] of the Cases. Males formed the Majority of the Victims contributing to 83.04% of the cases. Majority of the Victims were I the age group 31-40 years, consisting of 422 victims. The least Age Group affected were those below the age 10years and those individuals above the age 70years. Light Motor Vehicle were the Major Contributor to the Accidents, contributing to 46.40%[n-542] of the cases. Head and Neck was the Major region affected in 795 cases. The Maximum Fatality reported were due to Head or Craniocerebral Injuries in 87% of the cases. Abrasions were Present in all the Victims. Majority of the Deaths were due to Traumatic Shock reported in on the Spot Deaths or Brought Dead Victims in 35.45% [n-414] cases. The commonest Cause of Death reported after 07days of Treatment, were Septicemia, Lung infections, Peritonitis, Coma. Craniocerebral Injuries were the Main Contributors to Fatality in 87% of Accidents. Majority of Deaths were due to Traumatic Shocks due to Multiple injuries involved. Importance of Emergency Care is well understood in this study.

Retrospective study on fatal road traffic accident in Aizawl, Mizoram during 2014-2018

A traffic collision, also called a motor vehicle collision, car accident, occurs when a vehicle collides with another vehicle, pedestrian, animal, road debris, or any stationary obstruction, such as a tree, pole or building. Traffic collisions often result in injury, disability, death and damage to property as well as financial cost to both the society & individuals involved.

Urban population size and road traffic collisions in Europe

Millions of road traffic collisions take place every year, leading to significant knock-on effects. Many of these traffic collisions take place in urban areas, where traffic levels can be elevated. Yet, little is known about the extent to which urban population size impacts road traffic collision rates. Here, we use urban scaling models to analyse geographic and road traffic collision data from over 300 European urban areas in order to study this issue. Our results show that there is no significant change in the number of road traffic collisions per person for urban areas of different sizes. However, we find individual urban locations with traffic collision rates which are remarkably high. These findings have the potential to inform policies for the allocation of resources to prevent road traffic collisions across the different cities.