Real-time Secondary Crash Likelihood Prediction Excluding Post Primary Crash Features

TL;DR

This paper introduces a real-time secondary crash prediction framework that does not rely on post-crash features, using a hybrid machine learning approach to improve accuracy and practical applicability in traffic management.

Contribution

It develops a novel hybrid prediction framework with a dynamic spatiotemporal window and ensemble learning, enabling real-time secondary crash likelihood prediction without post-crash data.

Findings

Correctly identifies 91% of secondary crashes

Achieves an AUC of 0.952, outperforming previous models

Low false alarm rate of 0.20

Abstract

Secondary crash likelihood prediction is a critical component of an active traffic management system to mitigate congestion and adverse impacts caused by secondary crashes. However, existing approaches mainly rely on post-crash features (e.g., crash type and severity) that are rarely available in real time, limiting their practical applicability. To address this limitation, we propose a hybrid secondary crash likelihood prediction framework that does not depend on post-crash features. A dynamic spatiotemporal window is designed to extract real-time traffic flow and environmental features from primary crash locations and their upstream segments. The framework includes three models: a primary crash model to estimate the likelihood of secondary crash occurrence, and two secondary crash models to evaluate traffic conditions at crash and upstream segments under different comparative scenarios. An ensemble learning strategy integrating six machine learning algorithms is developed to enhance predictive performance, and a voting-based mechanism combines the outputs of the three models. Experiments on Florida freeways demonstrate that the proposed hybrid framework correctly identifies 91% of secondary crashes with a low false alarm rate of 0.20. The Area Under the ROC Curve improves from 0.654, 0.744, and 0.902 for the individual models to 0.952 for the hybrid model, outperforming previous studies.