Advance Real-time Detection of Traffic Incidents in Highways using Vehicle Trajectory Data

TL;DR

This paper presents a machine learning approach using vehicle trajectory data for early real-time detection of traffic incidents on highways, aiming to improve safety and reduce secondary crashes.

Contribution

It introduces a novel method of converting vehicle trajectories into uniform data for machine learning models to detect incidents in real-time.

Findings

Random Forest achieved the best incident prediction performance.

Trajectory data conversion improved model accuracy.

The approach enables proactive traffic incident management.

Abstract

A significant number of traffic crashes are secondary crashes that occur because of an earlier incident on the road. Thus, early detection of traffic incidents is crucial for road users from safety perspectives with a potential to reduce the risk of secondary crashes. The wide availability of GPS devices now-a-days gives an opportunity of tracking and recording vehicle trajectories. The objective of this study is to use vehicle trajectory data for advance real-time detection of traffic incidents on highways using machine learning-based algorithms. The study uses three days of unevenly sequenced vehicle trajectory data and traffic incident data on I-10, one of the most crash-prone highways in Louisiana. Vehicle trajectories are converted to trajectories based on virtual detector locations to maintain spatial uniformity as well as to generate historical traffic data for machine learning algorithms. Trips matched with traffic incidents on the way are separated and along with other trips with similar spatial attributes are used to build a database for modeling. Multiple machine learning algorithms such as Logistic Regression, Random Forest, Extreme Gradient Boost, and Artificial Neural Network models are used to detect a trajectory that is likely to face an incident in the downstream road section. Results suggest that the Random Forest model achieves the best performance for predicting an incident with reasonable recall value and discrimination capability.