🗺 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.

Real-Time Crash Identification using Connected Electric Vehicle Operation Data

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

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

For New-Taipei-City-Traffic-Safety-Hackathon

Application of ML algorithms in the context of traffic safety analysis

Empirical Bayes before-after study to evaluate ATSC safety effectiveness

Can features of the built environment be used to predict the locations of pedestrian crashes at the street level in Uptown Charlotte, North Carolina?

Circulate San Diego: Infrastructure Data Explorer (2023)

This paper develops methods to estimate the tail and full distribution of the lengths of the 0-intervals in a continuous time stationary ergodic stochastic process which takes the values 0 and 1 in alternating intervals. The methods are applied to the 100-car study, a big naturalistic driving experiment.

Tools for Traffic Accident Data in Japan