Analysis of the Statistical Methods for Vehicle Detection in the Accident Avoidance System an Application of ITS

Abstract

AbstractIntelligent Transport Systems consists of applications such as accident warning and avoidance systems. This is an area of interest to us in this research. The accident detection and alert system in the field of ITS (intelligent transportation system) are implemented and analyzed using many statistical methods for proving the accident detection point, GPS Coordinates points search, etc. In this paper, the analysis is performed on how accidents can be prevented using statistical analysis applied to the flow of vehicles in the construction area. This study helps to compare different statistical methods for vehicle detection in accident warning systems. Different statistical methods like Poisson’s probability distribution, Binomial distribution and Negative Binomial probability distributions were evaluated considering the count of vehicles and their distances from the area near or far from the prone area considered in the construction site. The aim of this paper is to evaluate and compare the statistical methods on the real-time datasets collected by hardware configuration of Arduino-Mega, Node-MCU and cloud service thingsspeak.com and shows that Poisson’s statistical distribution performs better than other methods in terms of probability of success. The different statistics like mean, standard deviation, and variance for different statistical methods are evaluated, out of these Poisson performs better.KeywordsAccident avoidanceIntelligent transportation system (ITS)Poisson’s distributionBinomial distributionNegative binomial distribution