Home / Current Issue / Paper 1716852
Traffic Accident Prediction Using Machine Learning Techniques
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I10-1716852
Abstract
Out there, right where rubber meets pavement, brightness stirs. This moment arrives. Not pausing first. Not once silence returns. At that point - the blacktop holds still. Forms rise as water runs down edges, dark turns follow trails. Pages once soaked with errors carried last names like stains. Not long before sirens tear into silence, a change slips beneath quiet things. As pressure builds toward snapping point, fragments fall into place - without anyone saying anything at all. Understanding shows up early, softening dread bit by bit. Pages worn thin by weather and grip showed trails of shaky handwriting. When confusion holds clues, scrambled digits unearth errors long forgotten. Gears whirl endlessly, ticking off moments - yet voices ask for truth that cuts fast. Out of Django comes a display shaping tangled outcomes into what feels real. Input today’s street truths, watch danger rise faster than breath can catch. Now, at first sign, problems showed up long before any wreck got close. Lightning-fast warnings hit local crews, triggering detours - occasionally barriers as well. Quiet hangs oddly within endless movement. In one click, all pieces link unexpectedly. City roads unfold on screen like they’ve belonged there forever
Keywords
Traffic Accident Prediction, Machine Learning, Random Forest, Data Analysis, Django Framework, Predictive Modeling, Road Safety, Classification Algorithms
How to cite this paper
@article{1716852,
author = {Niharika Chenna, G. Rupa, G. Madhu Shreya, Dr. M. Ramachandra},
title = {Traffic Accident Prediction Using Machine Learning Techniques},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {2821-2828},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716852.pdf},
abstract = {Out there, right where rubber meets pavement, brightness stirs. This moment arrives. Not pausing first. Not once silence returns. At that point - the blacktop holds still. Forms rise as water runs down edges, dark turns follow trails. Pages once soaked with errors carried last names like stains. Not long before sirens tear into silence, a change slips beneath quiet things. As pressure builds toward snapping point, fragments fall into place - without anyone saying anything at all. Understanding shows up early, softening dread bit by bit. Pages worn thin by weather and grip showed trails of shaky handwriting. When confusion holds clues, scrambled digits unearth errors long forgotten. Gears whirl endlessly, ticking off moments - yet voices ask for truth that cuts fast. Out of Django comes a display shaping tangled outcomes into what feels real. Input today’s street truths, watch danger rise faster than breath can catch. Now, at first sign, problems showed up long before any wreck got close. Lightning-fast warnings hit local crews, triggering detours - occasionally barriers as well. Quiet hangs oddly within endless movement. In one click, all pieces link unexpectedly. City roads unfold on screen like they’ve belonged there forever},
keywords = {Traffic Accident Prediction, Machine Learning, Random Forest, Data Analysis, Django Framework, Predictive Modeling, Road Safety, Classification Algorithms},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716852}
}