codes of Deep Spatio-Temporal Graph Convolutional Network for Traffic Accident Prediction

[IEEE T-ITS] MG-TAR: Multi-view Graph Convolutional Networks for Traffic Accident Risk Prediction

Traffic accident prediction using graph neural networks "TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks" (ACM SIGSPATIAL 2023)

Deep model for accident risk prediction over sparse traffic data

[WWW-21] DF-TAR: A Deep Fusion Network for Citywide Traffic Accident Risk Prediction with Dangerous Driving Behavior

A multimodal GNN framework for traffic accident prediction and causal analysis

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.

2022秋-同济大学软件学院-统计分析与建模期末项目

Road Traffic Accident prediction using Machine Learning Classification Technique

Do you know the probability of traffic accident in the future? Let us to evaluate it.

Dacon_AI_Competition

Traffic Accident Severity Prediction using Streamlit

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.

Data visualization and prediction tool on UK traffic accidents

Towards Smart Cities: Data-Driven Road Accident Prevention

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.

Real-time Accident Severity Prediction with Spatiotemporal Data

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This project predicts traffic accident severity using the 2023 STATS19 dataset. It employs machine learning and LIME to not only forecast outcomes like 'Fatal' or 'Slight' but also to provide clear, understandable explanations for its predictions, creating a powerful tool for enhancing road safety.

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

video classification using resnet resnet-50 is trained on street dataset images(dataset contains classes:robbery,accident,traffic jam,protest and normal).After training the network,resnet can classify the different events in video with high accuracy.There is no flickering problem during prediction.Also it can capture the images from video when prediction is accident in the input video.

Galvanize Data Science Capstone Project - Identifying features predictive of traffic accidents in Seattle

2020년 1학기 아주대학교 소프트웨어학과 기계학습및데이터마이닝 팀플 자료입니다.

This is a traffic severity prediction model built using XGBoost, deployed on flask.

My big exercise in Decision Support System at HUST 20192

A traffic accident casualty prediction model using Python

Master's thesis

대구 교통사고 피해 예측 AI 경진대회

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同济大学软件学院统计分析与建模期末项目