Spatiotemporal features of traffic help reduce automatic accident detection time
Automated Incident Detection (AID) pipeline as described in the paper. After the dataset generation phase (which is described in detail in our <a href"https://q-rai.github.io/publications/berres2024dib/">Data in Brief publication), we trained machine learning models on 70% of the data and kept the remaining 30% for the testing phase. We compared three different classifiers: logistic regression, random forest, and XGBoost.Abstract
This publication presents an annotated accident dataset which fuses traffic data from radar detection sensors, weather condition data, and light condition data with traffic accident data in a format that is easy to process using machine learning tools, databases, or data workflows. The purpose of this data is to analyze, predict, and detect traffic patterns when accidents occur. Each file contains a timeseries of traffic speeds, flows, and occupancies at the sensor nearest to the accident, as well as 5 neighboring sensors upstream and downstream. It also contains information about the accident type, date, and time. In addition to the accident data, we provide baseline data for typical traffic patterns during a given time of day. Overall, the dataset contains 6 months of annotated traffic data from November 2020 to April 2021. During this timeframe, and 361 accidents occurred in the monitored area around Chattanooga, Tennessee. This dataset served as the basis for a study on topology-aware automated accident detection for a companion publication.
Type
Publication
In Expert Systems with Applications
