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

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

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

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

Product-side Spec-Driven Development framework — PRD, interactive prototype, DDD handoff, AI runtime governance, acceptance gates. L0-L3 tiers, replaceable domain modules. Works with Claude Code, Cursor, Codex, OpenClaw.

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).

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

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

Cyclist safety risk assessment application for Austin using Random Forest classification with SMOTE for class balancing. Interactive Streamlit web app predicts cycling incident severity based on time, location, and environmental conditions to promote safer cycling practices.

Data analysis and interactive visualizations to evaluate young driver safety.

Data visualization of Sarasota, FL traffic accidents (2017–2022) using R

Real-time highway incident detection using YOLO for traffic monitoring and safety

Core codebase containing YOLOv7 and GRU model scripts supporting the AFODS manuscript.