Deep model for accident risk prediction over sparse traffic data

Road safety is one of the top most priority of every government and individual. Government spends billions of dollars on making great roadway infrastructure and safety certification of the vehicles, so that the lives of the people on the road will be safer. However, there are still a large number of fatal accidents occurring on the road. In the year 2018 alone, the number of fatalities on the road has increased upto 1.28 million [1]. Predicting accidents have thus become one of the widely researched topics which could be used by different agencies for optimizing traffic conditions (e.g. adding more lanes in one direction and reversing it when the condition changes), provide a dynamic route to riders using GPS and improving overall transportation infrastructure. There are a variety of dataset publicly available for this cause such as accident data, traffic event data and weather data. These datasets could be used to prepare useful classification models to predict whether a particular condition is more prone to accidents and drivers must drive with precaution.

Remaining useful life (RUL) prediction of cutting tools is critical to effective condition based maintenance for reducing downtime, ensuring quality and avoiding accidents. we worked on a research paper to build a Machine Learning model for predicting the remaining useful life of cutting tools. Extracted different features from the dataset and developed an algorithm by using Support Vector Regression for the prediction of RUL.

We hvae tried to predict the number of road accidents that can take place in india by using the road accidents dataset available on kaggle. This project uses Matplotlib for data visualization and scikits learn linear regression model for making prediction.

The United States Department of National Transportation and National Highway Traffic Safety Administration has provided a high quality of car-traffic accidents record which documents the initial details of the accidents where the National Transportation and Highway safety officers respond. The main goal of this project is to distribute, expand and ensure the safety concerns of the road trip carried out by various people by identifying the accidents and predicting the probability of accident occurrences all over the country in terms of accident time, cause, and severity with advanced tools in Machine Learning. With this data, we built 4 Machine learning models, compared those models using the performance metrics, and chose the best model for accident severity prediction. As a result, we made a prediction on the severity of traffic accidents that are most likely to occur in the United States.

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This project is designed to model road accidents based on traffic variables. title: "Generalized Additive Models (GAMs)" author: "AliMirzaei" date: "11/20/2022"

Simple Pytorch implementation of dashcam based accident prediction model

In developing nations such as India, the vehicular growth rate is increasing exponentially which is worsening the traffic operations. Most of the urban cities in India are facing traffic related problems such as congestion, accidents, pollution, etc. during peak hours. The main cause for traffic congestion in such cities is mainly due to uncontrolled urbanization and extensive usage of private vehicles. The traffic congestion leads to many problems like increase in travelling time, health disorders and accidents. Road accidents in India claimed over 1.5 lakh lives in the country in the year 2018, with over-speeding of vehicles being the major cause. The Ministry of Road Transport and Highways report on Road accidents in India stated that road accidents increased by a rate of 0.46 % in the year 2018 when compared to 2017 . Due to this there is a need to develop a model which can analyze and detect poor road conditions like potholes.This project aims in building a system which can detect the poor road conditions and can notify the driver as well as the government beforehand to improvise the road conditions. Pothole detection is being carried out using two techniques namely image processing and machine learning techniques. Those two techniques are used for a study of the detection and occurrence of potholes. In this project, we implemented both of them individually and then a combination of the techniques to see how image pre-processing can affect the performance of a deep learning model. The image pre-processing steps like erosion,median blur etc applied in this project removes the noise in the image which helps in better training of the model.First of all, we implemented the image processing techniques on a single image in the order: median blur, erosion, canny edge detection, contour detection, bounding box prediction. After that we labelled a dataset of around 800 images and passed it to the YOLOv5 model and noted the results.Secondly, we applied median blur on the already labelled dataset and then passed it to the YOLOv5 model and noted the results. Lastly, we applied median blur and erosion both and passed it to the model and noted the results. We compared the results at last. The conclusions reached are that a combination of machine learning and image processing techniques generates good performance in pothole detection and machine learning techniques provide better results than the usual image processing models.

A novel model based on Spatio-Temporal Hypergraph Convolutional Networks, designed to enhance the accuracy and efficiency of traffic accident prediction.

A traffic accident casualty prediction model using Python

Automobile Accident Risk Prediction via Twitter Sentiment Analysis and Geocoding (Tweepy, ArcGIS, Smote, NLP, ML models / NYC-based)

This repository contains the code implementation related to the research paper titled "Accident Impact Prediction based on a Deep Convolutional and Recurrent Neural Network Model." I share my bachelor project's code here!

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Accident Severity Prediction Model using Python

Predict the number of casualties of an accident in advance based on human (driver) and natural factors.

Final capstone of coursera data science professional certificate. Car accident severity prediction model.

This project predicts the Accident Casualty of an Individual from a given dataset which includes weather conditions , temperature , lighting conditions, etc. The approach for prediction of casualty severity has two steps: a. Cluster the data using agglomerative clustering algorithm, b. Prediction using lasso lars regression model.

Building Custom object detection model to detect the accidents using Google Cloud Vertex AI and java for preprocessing, integrating with GCP cloud and making calls to Prediction Model

Accurate prediction of severity of accidents can mitigate a considerable amount of road accidents and help saving time. Most of the analysis and prediction use a small dataset which leads to inaccuracies or more false positives. Here for analysis we have used a dataset that consists of around 2.4 million entries spread across the entire US. A prediction model using Random Forest Classifier is also built for the entire dataset to predict the severity of an accident.

A hybrid AI model for predicting failures in water distribution systems using Adaptive Neuro-Fuzzy Inference System (ANFIS). The model integrates Genetic Algorithms (GA) and Ant Colony Optimization (ACO) to improve the accuracy of accident prediction.

Incorporated Gradient Boosting tree Random Forest classifier and XGBoost models to predict severity of car accidents happened in United Stated from 2016 to 2020. Utilized Local Interpretable Model Agnostic Explanations to explain and interpret the prediction classes. Using EDA. found top 10 states and cities with greatest number of car accidents and its severity

An Air accident and aviation safety analysis based on events extracted from ICAO(International Civil Aviation Organization) API from jan2008 until may2022, along with an air crashes classified prediction model using Machine Learning

🚦 A Streamlit web app that predicts the likelihood of road accidents based on road and environmental conditions using a trained machine learning model. Enter road parameters manually and get real-time risk predictions powered by an ensemble learning model.

The aim of the project is to build prediction models to classify severity of road traffic accidents (slight injury, serious injury or fatal injury) based on various relevant information regarding the involved vehicles, drivers, casualties and surrounding conditions.

An interactive Streamlit web app that predicts the likelihood of road accidents based on environmental and road parameters. It uses trained machine learning models to provide mean risk predictions and a 95% confidence interval, updating live as you adjust inputs.

The aim of the project is to predict the severity of an accident due to various features like road conditions, geographic location, weather conditions and type of vehicles. The prediction is achieved using three models- Naive Bayes, Suport Vector Machine and Neural Network and the three models are compared based on the accuracy score.

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Analysis of Road Safety Data and Accident Severity Prediction Using Deep Learning Model

Predicting the real-time severity of accidents and studying accident hot-spot locations in the US using Python and Tableau: