Statistical Analysis and Development of Accident Prediction Model of Road Safety Conditions in Hisar City

Abstract

The severity of road accidents is a big problem around the world, particularly in developing countries. Recognizing the major contributing variables can help reduce the severity of traffic accidents. This research uncovered new information as well as the most substantial target-specific factors related to the severity of road accidents. T-stat, P-value, Significance and other test values are determined to check the dependency of dependent variable on independent variable in order to obtain the most significant road accident variables. In this research, a comparative analysis of accident data from Hisar and Haryana are compared. According to the findings, Haryana’s accident severity index (46.20) was higher in 2019 than Hisar’s (36.01), while Hisar had fewer accidents per lakh population (33.34) than Haryana (38.40). The outcomes of the study were used to develop an effective and precise accident predicting model is developed for Hisar city and state Haryana using a statistical method. Four models were created using linear regression analysis, two each for Hisar and Haryana. These models produce good results with a margin of error that is within acceptable bounds (0-5%), allowing them to be used to predict future traffic accidents and deaths.

Estimation of Vulnerable Road User Accident Frequency through the Soft Computing Models

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Forecasting Road Accidental Deaths in India: An Explicit Comparison between ARIMA and Exponential Smoothing Method

“…To define predictive modelling, it is the process of using familiar results to generate, process, and validate a model that is used to forecast future events and outcomes. Regression (Bamel et al, 2021) and neural networks (Zhang & Yuan, 2023) are two of the most widely used predictive modelling techniques. Other techniques include hidden Markov modeling (Laverty et al, 2002) time series data mining, decision trees, and Bayesian analysis.…”