This repository offers code to reuse methodology and repeat experiments in the study "A Unified Probabilistic Approach to Traffic Conflict Detection".

This repository shares information and guidelines to use the reconstructed trajectories of naturalistic crashes and near-crashes in the SHRP2 NDS.

🗺 This is a service class application software that for the poor areas which have bad traffic safety,the crowd which have lower safety awareness and the people which go out to an unfamiliar place.

This is the source codes and modeling data for paper: LSTM + Transformer Real-Time Crash Risk Evaluation Using Traffic Flow and Risky Driving Behavior Data

SAVeD (Social media-based ADAS-equipped Vehicle event Dataset) shaoyan.zhai@ucf.edu

警察庁が公開している、交通事故統計情報のオープンデータの2024年の本票をコード表をもとに読みやすい形式（GISデータ）に変換するプログラム

警察庁が公開している、交通事故統計情報のオープンデータの2023年の本票をコード表をもとに読みやすい形式（GISデータ）に変換するプログラム

Real-Time Crash Identification using Connected Electric Vehicle Operation Data

A novel spatial Machine Learning (ML) framework that integrates multiple ML models—Random Forest, XGBoost, and LightGBM—with geographically weighted regression to account for spatial heterogeneity. The framework has been successfully applied to intersection crash frequency modeling, achieving excellent performance.

Vision-based adaptive crosswalk system that extends pedestrian green time using YOLO tracking, homography speed estimation, and safety logic to protect elderly and slow walkers in real time.

Real-time injury severity prediction for the eCall system. Features an end-to-end pipeline, CatBoost inference, and a live Streamlit dashboard.

Using Predictive Analytics to Improve Traffic Safety, by Team Knight Rider of NYU Stern MSBA Class of 2019

An end-to-end Exploratory Data Analysis (EDA) of road traffic accidents in Edinburgh using R, Quarto, and interactive Leaflet mapping.

警察庁 交通事故データを地域メッシュ単位で集計するプログラム

YOLO^2 is an inference pipeline using two YOLO models in succession. It is designed for detecting motorcycles with the number of passengers, and how many of these are wearing helmets.

This is the WinBUGS codes for AMAR paper: https://doi.org/10.1016/j.amar.2025.100387. It includes codes for running Grouped Random Parameters Poisson-Lindley model with spatial effects and other baseline models (e.g., Grouped Random Parameters Poisson-Lindley model).

Vision Zero Report Card — 7 years of NHTSA FARS truck-involved fatalities across 19 US cities. Reproducible Python pipeline + filtered dataset under CC BY 4.0.

Shiny app of accidents involving cyclists in and around Mannheim for ADFC Mannheim.

Interaktive Unfallkarten-Werkbank: Visualisierung, Filterung und Auswertung deutscher Unfallatlas-Daten mit Export nach CSV, GeoJSON & KML

Data analysis and interactive visualizations to evaluate young driver safety.